Paper Digest: AAAI 2016 Highlights
The AAAI Conference on Artificial Intelligence (AAAI) is one of the top artificial intelligence conferences in the world. In 2016, it is to be held in Phoenix, Arizona.
To help AI community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.
We thank all authors for writing these interesting papers, and readers for reading our digests. If you do not want to miss any interesting AI paper, you are welcome to sign up our free paper digest service to get new paper updates customized to your own interests on a daily basis.
Paper Digest Team
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TABLE 1: AAAI 2016 Papers
| Title | Authors | Highlight | |
|---|---|---|---|
| 1 | Inferring Multi-Dimensional Ideal Points for US Supreme Court Justices | Mohammad Raihanul Islam, K. S. M. Tozammel Hossain, Siddharth Krishnan, Naren Ramakrishnan | We present an automated approach to infer such ideal points for justices of the US Supreme Court. |
| 2 | Little Is Much: Bridging Cross-Platform Behaviors through Overlapped Crowds | Meng Jiang, Peng Cui, Nicholas Jing Yuan, Xing Xie, Shiqiang Yang | In this paper, we propose a novel semi-supervised transfer learning method to address the problem of cross-platform behavior prediction, called XPTrans. |
| 3 | Scientific Ranking over Heterogeneous Academic Hypernetwork | Ronghua Liang, Xiaorui Jiang | This paper proposes a novel mutual ranking algorithm based on the multinomial heterogeneous academic hypernetwork, which serves as a generalized model of a scientific literature database. |
| 4 | MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-Based Protein Structure Prediction | Zeming Lin, Jack Lanchantin, Yanjun Qi | Taking inspiration from the image classification domain we propose a deep convolutional neural network architecture, MUST-CNN, to predict protein properties. |
| 5 | Hospital Stockpiling Problems with Inventory Sharing | Eric Lofgren, Anil Vullikanti | In this paper, we formalize hospital stockpiling as a game-theoretical problem. |
| 6 | Predicting ICU Mortality Risk by Grouping Temporal Trends from a Multivariate Panel of Physiologic Measurements | Yuan Luo, Yu Xin, Rohit Joshi, Leo Celi, Peter Szolovits | We aim to improve both accuracy and interpretability of prediction models by introducing Subgraph Augmented Non-negative Matrix Factorization (SANMF) on ICU physiologic time series. |
| 7 | Learning to Generate Posters of Scientific Papers | Yuting Qiang, Yanwei Fu, Yanwen Guo, Zhi-Hua Zhou, Leonid Sigal | In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To learn and validate our model, we collect and make public a Poster-Paper dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. |
| 8 | Face Behind Makeup | Shuyang Wang, Yun Fu | In this work, we propose a novel automatic makeup detector and remover framework. Moreover, we build a stepwise makeup dataset (SMU) which to the best of our knowledge is the first dataset with procedures of makeup. |
| 9 | Social Role-Aware Emotion Contagion in Image Social Networks | Yang Yang, Jia Jia, Boya Wu, Jie Tang | In this paper, we study an interesting problem of emotion contagion in social networks. |
| 10 | Survival Prediction by an Integrated Learning Criterion on Intermittently Varying Healthcare Data | Jianfei Zhang, Lifei Chen, Alain Vanasse, Josiane Courteau, Shengrui Wang | For this reason, we propose a new semi-proportional hazards model using locally time-varying coefficients, and a novel complete-data model learning criterion for coefficient optimization. |
| 11 | On the Minimum Differentially Resolving Set Problem for Diffusion Source Inference in Networks | Chuan Zhou, Wei-Xue Lu, Peng Zhang, Jia Wu, Yue Hu, Li Guo | In this paper we theoretically study the minimum Differentially Resolving Set (DRS) problem derived from the classical sensor placement optimization problem in network source locating. |
| 12 | From Tweets to Wellness: Wellness Event Detection from Twitter Streams | Mohammad Akbari, Xia Hu, Nie Liqiang, Tat-Seng Chua | Existing approaches for event extraction are not applicable to personal wellness events due to its domain nature characterized by plenty of noise and variety in data, insufficient samples, and inter-relation among events.To tackle these problems, we propose an optimization learning framework that utilizes the content information of microblogging messages as well as the relations between event categories. |
| 13 | “8 Amazing Secrets for Getting More Clicks”: Detecting Clickbaits in News Streams Using Article Informality | Prakhar Biyani, Kostas Tsioutsiouliklis, John Blackmer | In this paper, we present a machine-learning model to detect clickbaits. |
| 14 | Business-Aware Visual Concept Discovery from Social Media for Multimodal Business Venue Recognition | Bor-Chun Chen, Yan-Ying Chen, Francine Chen, Dhiraj Joshi | To this end, we propose a novel framework for business venue recognition. |
| 15 | Capturing Semantic Correlation for Item Recommendation in Tagging Systems | Chaochao Chen, Xiaolin Zheng, Yan Wang, Fuxing Hong, Deren Chen | Thus these methods cannot deal with the data sparsity without commonly rated items (DS-WO-CRI) problem, limiting their recommendation performance. |
| 16 | Identifying Sentiment Words Using an Optimization Model with L1 Regularization | Zhi-Hong Deng, Hongliang Yu, Yunlun Yang | In this paper, we propose an optimization model with L1 regularization, called ISOMER, for identifying the sentiment words from the corpus. |
| 17 | Community-Based Question Answering via Heterogeneous Social Network Learning | Hanyin Fang, Fei Wu, Zhou Zhao, Xinyu Duan, Yueting Zhuang, Martin Ester | In this paper, we propose a novel framework which encodes not only the contents of question-answer(Q-A) but also the social interaction cues in the community to boost the cQA tasks. |
| 18 | College Towns, Vacation Spots, and Tech Hubs: Using Geo-Social Media to Model and Compare Locations | Hancheng Ge, James Caverlee | In this paper, we explore the potential of geo-social media to construct location-based interest profiles to uncover the hidden relationships among disparate locations. |
| 19 | Inferring a Personalized Next Point-of-Interest Recommendation Model with Latent Behavior Patterns | Jing He, Xin Li, Lejian Liao, Dandan Song, William K. Cheung | In this paper, we address the problem of personalized next Point-of-interest (POI) recommendation which has become an important and very challenging task in location-based social networks (LBSNs), but not well studied yet. |
| 20 | VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback | Ruining He, Julian McAuley | In this paper we propose a scalable factorization model to incorporate visual signals into predictors of people’s opinions, which we apply to a selection of large, real-world datasets. |
| 21 | Improved Neural Machine Translation with SMT Features | Wei He, Zhongjun He, Hua Wu, Haifeng Wang | In order to solve the above problems, we incorporate statistical machine translation (SMT) features, such as a translation model and an n-gram language model, with the NMT model under the log-linear framework. |
| 22 | A Scalable Framework to Choose Sellers in E-Marketplaces Using POMDPs | Athirai A. Irissappane, Frans A. Oliehoek, Jie Zhang | In this paper, we propose the Mixture of POMDP Experts (MOPE) technique, which exploits the inherent structure of trust-based domains, such as the seller selection problem in e-markets, by aggregating the solutions of smaller sub-POMDPs. |
| 23 | Fusing Social Networks with Deep Learning for Volunteerism Tendency Prediction | Yongpo Jia, Xuemeng Song, Jingbo Zhou, Li Liu, Liqiang Nie, David S. Rosenblum | In this paper, we study a challenging task for integrating users’ information from multiple heterogeneous social networks to gain a comprehensive understanding of users’ interests and behaviors. |
| 24 | Detect Overlapping Communities via Ranking Node Popularities | Di Jin, Hongcui Wang, Jianwu Dang, Dongxiao He, Weixiong Zhang | Here we extend the stochastic model method to detection of overlapping communities with the virtue of autonomous determination of the number of communities. |
| 25 | Top-N Recommender System via Matrix Completion | Zhao Kang, Chong Peng, Qiang Cheng | In this paper, we propose a simple yet promising algorithm. |
| 26 | Robust Text Classification in the Presence of Confounding Bias | Virgile Landeiro, Aron Culotta | In this paper, we consider the case where there is a confounding variable Z that influences both the text features X and the class variable Y. For example, a classifier trained to predict the health status of a user based on their online communications may be confounded by socioeconomic variables. |
| 27 | Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts | Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan | In this paper, we extend RNN and propose a novel method called Spatial Temporal Recurrent Neural Networks (ST-RNN). |
| 28 | Fortune Teller: Predicting Your Career Path | Ye Liu, Luming Zhang, Liqiang Nie, Yan Yan, David S. Rosenblum | But rather than rely on “black arts” to make predictions, in this work we scientifically and systematically study the feasibility of career path prediction from social network data. |
| 29 | Predicting Online Protest Participation of Social Media Users | Suhas Ranganath, Fred Morstatter, Xia Hu, Jiliang Tang, Suhang Wang, Huan Liu | In this paper, we are inspired by sociological theories of protest participation and propose a framework to predict from the user’s past status messages and interactions whether the next post of the user will be a declaration of protest. |
| 30 | Context-Sensitive Twitter Sentiment Classification Using Neural Network | Yafeng Ren, Yue Zhang, Meishan Zhang, Donghong Ji | Sentiment classification on Twitter has attracted increasing research in recent years.Most existing work focuses on feature engineering according to the tweet content itself.In this paper, we propose a context-based neural network model for Twitter sentiment analysis, incorporating contextualized features from relevant Tweets into the model in the form of word embedding vectors.Experiments on both balanced and unbalanced datasets show that our proposed models outperform the current state-of-the-art. |
| 31 | ClaimEval: Integrated and Flexible Framework for Claim Evaluation Using Credibility of Sources | Mehdi Samadi, Partha Talukdar, Manuela Veloso, Manuel Blum | In this paper, we present ClaimEval, a novel and integrated approach which given a set of claims to validate, extracts a set of pro and con arguments from the Web information sources, and jointly estimates credibility of sources and correctness of claims. |
| 32 | On the Effectiveness of Linear Models for One-Class Collaborative Filtering | Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, Darius Braziunas | In this paper, we propose LRec, a variant of SLIM that overcomes these limitations without sacrificing any of SLIM’s strengths.At its core, LRec employs linear logistic regression; despite this simplicity, LRec consistently and significantly outperforms all existing methods on a range of datasets. |
| 33 | Supervised Hashing via Uncorrelated Component Analysis | SungRyull Sohn, Hyunwoo Kim, Junmo Kim | In this paper, we propose a novel projection-based hashing method that attempts to maximize the precision and recall. |
| 34 | Commonsense in Parts: Mining Part-Whole Relations from the Web and Image Tags | Niket Tandon, Charles Hariman, Jacopo Urbani, Anna Rohrbach, Marcus Rohrbach, Gerhard Weikum | This paper presents a new method for automatically acquiring part-whole commonsense from Web contents and image tags at an unprecedented scale, yielding many millions of assertions, while specifically addressing the four shortcomings of prior work. |
| 35 | Recommendation with Social Dimensions | Jiliang Tang, Suhang Wang, Xia Hu, Dawei Yin, Yingzhou Bi, Yi Chang, Huan Liu | In this paper, we investigate how to exploit the heterogeneity of social relations and weak dependency connections for recommendation. |
| 36 | Column-Oriented Datalog Materialization for Large Knowledge Graphs | Jacopo Urbani, Ceriel Jacobs, Markus Krötzsch | In this paper, we present a new method of materializing Datalog inferences, which combines a column-based memory layout with novel optimization methods that avoid redundant inferences at runtime. |
| 37 | Semantic Community Identification in Large Attribute Networks | Xiao Wang, Di Jin, Xiaochun Cao, Liang Yang, Weixiong Zhang | We propose a novel nonnegative matrix factorization (NMF) model with two sets of parameters, the community membership matrix and community attribute matrix, and present efficient updating rules to evaluate the parameters with a convergence guarantee. |
| 38 | Unfolding Temporal Dynamics: Predicting Social Media Popularity Using Multi-scale Temporal Decomposition | Bo Wu, Tao Mei, Wen-Huang Cheng, Yongdong Zhang | In this paper, we present a novel approach to factorize the popularity into user-item context and time-sensitive context for exploring the mechanism of dynamic popularity. |
| 39 | Modeling Users’ Preferences and Social Links in Social Networking Services: A Joint-Evolving Perspective | Le Wu, Yong Ge, Qi Liu, Enhong Chen, Bai Long, Zhenya Huang | We propose a probabilistic approach to fuse these social theories for jointly modeling users’ temporal behaviors in SNSs. |
| 40 | Cross-Lingual Taxonomy Alignment with Bilingual Biterm Topic Model | Tianxing Wu, Guilin Qi, Haofen Wang, Kang Xu, Xuan Cui | In this paper, we present a new approach to deal with the problem of cross-lingual taxonomy alignment without using any domain-specific information. |
| 41 | Online Cross-Modal Hashing for Web Image Retrieval | Liang Xie, Jialie Shen, Lei Zhu | In this paper, we propose Online Cross-modal Hashing (OCMH) which can effectively address the above two problems by learning the shared latent codes (SLC). |
| 42 | Understanding Emerging Spatial Entities | Jinyoung Yeo, Jin-woo Park, Seung-won Hwang | Our goal is thus to address this limitation by identifying effective linking techniques for emerging spatial entities and photos. |
| 43 | Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark | Quanzeng You, Jiebo Luo, Hailin Jin, Jianchao Yang | In this work, we introduce a new data set, which started from 3+ million weakly labeled images of different emotions and ended up 30 times as large as the current largest publicly available visual emotion data set. |
| 44 | STELLAR: Spatial-Temporal Latent Ranking for Successive Point-of-Interest Recommendation | Shenglin Zhao, Tong Zhao, Haiqin Yang, Michael R. Lyu, Irwin King | To capture the impact of time on successive POI recommendation, in this paper, we propose a spatial-temporal latent ranking (STELLAR) method to explicitly model the interactions among user, POI, and time. |
| 45 | Learning the Preferences of Ignorant, Inconsistent Agents | Owain Evans, Andreas Stuhlmueller, Noah Goodman | We present a behavioral experiment in which human subjects perform preference inference given the same observations of choices as our model. |
| 46 | Egocentric Video Search via Physical Interactions | Taiki Miyanishi, Jun-ichiro Hirayama, Quan Kong, Takuya Maekawa, Hiroki Moriya, Takayuki Suyama | In this paper, we propose a gesture-based egocentric video retrieval framework, which retrieves past visual experience using body gestures as non-verbal queries. |
| 47 | Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference | Quanjun Chen, Xuan Song, Harutoshi Yamada, Ryosuke Shibasaki | Therefore, in this paper, we collect big and heterogeneous data (7 months traffic accident data and 1.6 million users’ GPS records) to understand how human mobility will affect traffic accident risk. |
| 48 | Autonomous Electricity Trading Using Time-of-Use Tariffs in a Competitive Market | Daniel Urieli, Peter Stone | We formalize the problem of TOU tariff optimization and propose an algorithm for approximating its solution. |
| 49 | Reuse of Neural Modules for General Video Game Playing | Alexander Braylan, Mark Hollenbeck, Elliot Meyerson, Risto Miikkulainen | A general approach to knowledge transfer is introduced in which an agent controlled by a neural network adapts how it reuses existing networks as it learns in a new domain. |
| 50 | Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games Using Convolutional Networks | Nikolai Yakovenko, Liangliang Cao, Colin Raffel, James Fan | The contributions of this paper include: (1) a novel represen- tation for poker games, extendable to different poker vari- ations, (2) a Convolutional Neural Network (CNN) based learning model that can effectively learn the patterns in three different games, and (3) a self-trained system that signif- icantly beats the heuristic-based program on which it is trained, and our system is competitive against human expert players. |
| 51 | Computing Possible and Necessary Equilibrium Actions (and Bipartisan Set Winners) | Markus Brill, Rupert Freeman, Vincent Conitzer | In this paper, we formalize this by considering incompletely specified games, in which some entries of the payoff matrices can be chosen from a specified set. |
| 52 | From Duels to Battlefields: Computing Equilibria of Blotto and Other Games | AmirMahdi Ahmadinejad, Sina Dehghani, MohammadTaghi Hajiaghay, Brendan Lucier, Hamid Mahini, Saeed Seddighin | In this paper we show how to compute equilibria of Colonel Blotto games. |
| 53 | Maximizing Revenue with Limited Correlation: The Cost of Ex-Post Incentive Compatibility | Michael Albert, Vincent Conitzer, Giuseppe Lopomo | In this paper, we explore the implications of Bayesian versus ex-post IC in a correlated valuation setting. |
| 54 | Blind, Greedy, and Random: Algorithms for Matching and Clustering Using Only Ordinal Information | Elliot Anshelevich, Shreyas Sekar | We study the Maximum Weighted Matching problem in a partial information setting where the agents’ utilities for being matched to other agents are hidden and the mechanism only has access to ordinal preference information. |
| 55 | Strategyproof Peer Selection: Mechanisms, Analyses, and Experiments | Haris Aziz, Omer Lev, Nicholas Mattei, Jeffrey S. Rosenschein, Toby Walsh | We propose a new strategyproof (impartial) mechanism called Dollar Partition that satisfies desirable axiomatic properties. |
| 56 | A Security Game Combining Patrolling and Alarm-Triggered Responses Under Spatial and Detection Uncertainties | Nicola Basilico, Giuseppe De Nittis, Nicola Gatti | Motivated by a number of security applications, among which border patrolling, we study, to the best of our knowledge, the first Security Game model in which patrolling strategies need to be combined with responses to signals raised by an alarm system, which is spatially uncertain (i.e., it is uncertain over the exact location the attack is ongoing) and is affected by false negatives (i.e., the missed detection rate of an attack may be positive). |
| 57 | Learning Market Parameters Using Aggregate Demand Queries | Xiaohui Bei, Wei Chen, Jugal Garg, Martin Hoefer, Xiaoming Sun | We study efficient algorithms for a natural learning problem in markets. |
| 58 | An Algorithmic Framework for Strategic Fair Division | Simina Brânzei, Ioannis Caragiannis, David Kurokawa, Ariel D. Procaccia | To address this question we adopt a novel algorithmic approach, proposing a concrete computational model and reasoning about the game-theoretic properties of algorithms that operate in this model. |
| 59 | One Size Does Not Fit All: A Game-Theoretic Approach for Dynamically and Effectively Screening for Threats | Matthew Brown, Arunesh Sinha, Aaron Schlenker, Milind Tambe | We address this challenge with the following contributions: (1) a threat screening game (TSG) model for general screening domains; (2) an NP-hardness proof for computing the optimal strategy of TSGs; (3) a scheme for decomposing TSGs into subgames to improve scalability; (4) a novel algorithm that exploits a compact game representation to efficiently solve TSGs, providing the optimal solution under certain conditions; and (5) an empirical comparison of our proposed algorithm against the current state-of-the-art optimal approach for large-scale game-theoretic resource allocation problems. |
| 60 | Strategy-Based Warm Starting for Regret Minimization in Games | Noam Brown, Tuomas Sandholm | We introduce the first general, principled method for warm starting CFR. |
| 61 | Using Correlated Strategies for Computing Stackelberg Equilibria in Extensive-Form Games | Jiri Cermak, Branislav Bosansky, Karel Durkota, Viliam Lisy, Christopher Kiekintveld | We present a new algorithm for computing SSE for two-player extensive-form general-sum games with imperfect information (EFGs) where computing SSE is an NP-hard problem. |
| 62 | Assignment and Pricing in Roommate Market | Pak Hay Chan, Xin Huang, Zhengyang Liu, Chihao Zhang, Shengyu Zhang | We introduce a roommate market model, in which 2n people need to be assigned to n rooms, with two people in each room. |
| 63 | Incentives for Strategic Behavior in Fisher Market Games | Ning Chen, Xiaotie Deng, Bo Tang, Hongyang Zhang | We investigate the extent to which an agent’s utility can be increased by unilateral strategic plays and prove that the percentage of this improvement is at most 2 for markets with weak gross substitute utilities. |
| 64 | Rules for Choosing Societal Tradeoffs | Vincent Conitzer, Rupert Freeman, Markus Brill, Yuqian Li | We introduce the family of distance-based rules and show that these can be justified as maximum likelihood estimators of the truth. |
| 65 | Judgment Aggregation under Issue Dependencies | Marco Costantini, Carla Groenland, Ulle Endriss | We introduce a new family of judgment aggregation rules, called the binomial rules, designed to account for hidden dependencies between some of the issues being judged. |
| 66 | Price of Pareto Optimality in Hedonic Games | Edith Elkind, Angelo Fanelli, Michele Flammini | In this paper, we argue that Pareto optimality can be seen as a notion of stability, and introduce the concept of Price of Pareto Optimality: this is an analogue of the Price of Anarchy, where the maximum is computed over the class of Pareto optimal outcomes, i.e., outcomes that do not permit a deviation by the grand coalition that makes all players weakly better off and some players strictly better off. |
| 67 | Multiwinner Analogues of the Plurality Rule: Axiomatic and Algorithmic Perspectives | Piotr Faliszewski, Piot Skowron, Arkadii Slinko, Nimrod Talmon | We characterize the class of committee scoring rules that satisfy the fixed-majority criterion. |
| 68 | Ad Auctions and Cascade Model: GSP Inefficiency and Algorithms | Gabriele Farina, Nicola Gatti | In this paper, we provide some contributions to answer these questions. |
| 69 | Variations on the Hotelling-Downs Model | Michal Feldman, Amos Fiat, Svetlana Obraztsova | In this paper we expand the standard Hotelling-Downs model of spatial competition to a setting where clients do not necessarily choose their closest candidate (retail product or political). |
| 70 | A Geometric Method to Construct Minimal Peer Prediction Mechanisms | Rafael Frongillo, Jens Witkowski | In this paper, we use a geometric perspective to prove that minimal peer prediction mechanisms are equivalent to power diagrams, a type of weighted Voronoi diagram. |
| 71 | Sequence-Form and Evolutionary Dynamics: Realization Equivalence to Agent Form and Logit Dynamics | Nicola Gatti, Marcello Restelli | In this paper, we focus on dynamics for the sequence form of extensive-form games, providing three dynamics: one realization equivalent to the normal-form logit dynamic, one realization equivalent to the agent-form replicator dynamic, and one realization equivalent to the agent-form logit dynamic. |
| 72 | Who Can Win a Single-Elimination Tournament? | Michael P. Kim, Warut Suksompong, Virginia Vassilevska Williams | A natural and well-studied question is the tournament fixing problem (TFP): given the set of all pairwise match outcomes, can a tournament organizer rig an SE tournament by adjusting the initial seeding so that their favorite player wins? |
| 73 | When Can the Maximin Share Guarantee Be Guaranteed? | David Kurokawa, Ariel D. Procaccia, Junxing Wang | Our goal is to understand when we can expect to be able to give each player his MMS guarantee. |
| 74 | Multi-Attribute Proportional Representation | Jérôme Lang, Piotr Krzysztof Skowron | We consider the following problem in which a given number of items has to be chosen from a predefined set. |
| 75 | Multi-Defender Strategic Filtering Against Spear-Phishing Attacks | Aron Laszka, Jian Lou, Yevgeniy Vorobeychik | We therefore consider the problem of strategic threshold-selection by a collection of independent self-interested users. |
| 76 | Counterfactual Regret Minimization in Sequential Security Games | Viliam Lisy, Trevor Davis, Michael Bowling | We propose an adaptation of the CFR+ algorithm for NFGSS and compare its performance to the standard methods based on linear programming and incremental game generation. |
| 77 | Optimizing Trading Assignments in Water Right Markets | Yicheng Liu, Pingzhong Tang, Tingting Xu, Hang Zheng | Our goal is to maximize the transaction volume or welfare. |
| 78 | On the Complexity of mCP-nets | Thomas Lukasiewicz, Enrico Malizia | In this paper, we start to fill this gap by carrying out a precise computational complexity analysis of voting tasks on acyclic binary polynomially connectedmCP-nets whose constituents are standard CP-nets. |
| 79 | Reinstating Combinatorial Protections for Manipulation and Bribery in Single-Peaked and Nearly Single-Peaked Electorates | Vijay Menon, Kate Larson | In light of these results, we investigate whether it is possible to reimpose NP-hardness shields for such electorates by allowing the voters to specify partial preferences instead of insisting they cast complete ballots. |
| 80 | Refining Subgames in Large Imperfect Information Games | Matej Moravcik, Martin Schmid, Karel Ha, Milan Hladik, Stephen J. Gaukrodger | To prevent this, we introduce the notion of subgame margin, a simple value with appealing properties. |
| 81 | Complexity of Hedonic Games with Dichotomous Preferences | Dominik Peters | In this work, we study the computational complexity of questions related to finding optimal and stable partitions in dichotomous hedonic games under various ways of restricting and representing the collection of approved coalitions. |
| 82 | Graphical Hedonic Games of Bounded Treewidth | Dominik Peters | We introduce the notion of a graphical hedonic game and show that, in contrast, on classes of graphical hedonic games whose underlying graphs are of bounded treewidth and degree, such problems become easy. |
| 83 | Preferences Single-Peaked on Nice Trees | Dominik Peters, Edith Elkind | Trick (1989) proposed a polynomial-time algorithm that finds some tree with respect to which a given preference profile is single-peaked. |
| 84 | Fast Optimal Clearing of Capped-Chain Barter Exchanges | Benjamin Plaut, John P. Dickerson, Tuomas Sandholm | The leading algorithms for this optimization problem use either branch and price — a combination of branch and bound and column generation — or constraint generation. |
| 85 | Optimal Aggregation of Uncertain Preferences | Ariel D. Procaccia, Nisarg Shah | Under the classic objective of minimizing the (expected) sum of Kendall tau distances between the input rankings and the output ranking, we establish that preference elicitation is surprisingly straightforward and near-optimal solutions can be obtained in polynomial time. |
| 86 | False-Name-Proof Locations of Two Facilities: Economic and Algorithmic Approaches | Akihisa Sonoda, Taiki Todo, Makoto Yokoo | This paper considers a mechanism design problem for locating two identical facilities on an interval, in which an agent can pretend to be multiple agents. |
| 87 | Closeness Centrality for Networks with Overlapping Community Structure | Mateusz K. Tarkowski, Piotr Szczepański, Talal Rahwan, Tomasz P. Michalak, Michael Wooldridge | Our aim is to bridge this gap. |
| 88 | Computing Rational Decisions In Extensive Games With Limited Foresight | Paolo Turrini | We introduce a class of extensive form games whereplayers might not be able to foresee the possible consequences of their decisions and form a model of theiropponents which they exploit to achieve a more profitable outcome. |
| 89 | Computing Optimal Monitoring Strategy for Detecting Terrorist Plots | Zhen Wang, Yue Yin, Bo An | This paper makes five key contributions toward the challenging problem of computing optimal monitoring strategies: 1) A new Stackelberg game model for terrorist plot detection;2) A modified double oracle framework for computing the optimal strategy effectively;3) Complexity results for both defender and attacker oracle problems;4) Novel mixed-integer linear programming (MILP) formulations for best response problems of both players;and 5) Effective approximation algorithms for generating suboptimal responses for both players.Experimental evaluation shows that our approach can obtain a robust enough solution outperforming widely-used centrality based heuristics significantly and scale up to realistic-sized problems. |
| 90 | Quantitative Extensions of the Condorcet Jury Theorem with Strategic Agents | Lirong Xia | We initiate a research agenda of quantitatively extend- ing the Jury Theorem with strategic agents by characterizing the price of anarchy (PoA) and the price of stability (PoS) of the common interest Bayesian voting games for three classes of mechanisms: plurality, MAPs, and the mechanisms that satisfy anonymity, neutrality, and strategy-proofness (w.r.t. a set of natural probabil- ity models). |
| 91 | Lift-Based Bidding in Ad Selection | Jian Xu, Xuhui Shao, Jianjie Ma, Kuang-chih Lee, Hang Qi, Quan Lu | In this paper, we propose a new bidding strategy and prove that if the bid price is decided based on the performance lift rather than absolute performance value, advertisers can actually gain more action events. |
| 92 | Optimizing Personalized Email Filtering Thresholds to Mitigate Sequential Spear Phishing Attacks | Mengchen Zhao, Bo An, Christopher Kiekintveld | For single-credential scenarios, we demonstrate that the optimal defense strategy can be found by solving a binary combinatorial optimization problem called PEDS. |
| 93 | Unsupervised Feature Selection by Heuristic Search with Provable Bounds on Suboptimality | Hiromasa Arai, Crystal Maung, Ke Xu, Haim Schweitzer | We propose a similar approach related to the Weighted A* algorithm. |
| 94 | Tiebreaking Strategies for A* Search: How to Explore the Final Frontier | Masataro Asai, Alex Fukunaga | We develop a new framework for tiebreaking based on a depth metric which measures distance from the entrance to the plateau, and propose a new, randomized strategy which significantly outperforms standard strategies on domains with zero-cost actions. |
| 95 | CAPReS: Context Aware Persona Based Recommendation for Shoppers | Joydeep Banerjee, Gurulingesh Raravi, Manoj Gupta, Sindhu K. Ernala, Shruti Kunde, Koustuv Dasgupta | This work considers the problem of persona based shopping recommendation for such stores to maximize the value for money of the shoppers. |
| 96 | Nested Monte Carlo Search for Two-Player Games | Tristan Cazenave, Abdallah Saffidine, Michael Schofield, Michael Thielscher | We seek to improve the quality of information extracted from the Monte Carlo playout in three ways. |
| 97 | Look-Ahead with Mini-Bucket Heuristics for MPE | Rina Dechter, Kalev Kask, William Lam, Javier Larrosa | We present and analyze the complexity of computing the residual (a.k.a Bellman update) of the Mini-Bucket heuristic and show how this can be used to identify which parts of the search space are more likely to benefit from look-ahead and how to bound its overhead. |
| 98 | Solving the Station Repacking Problem | Alexandre Fréchette, Neil Newman, Kevin Leyton-Brown | We investigate the problem of repacking stations in the FCC’s upcoming, multi-billion-dollar “incentive auction”. |
| 99 | The Complexity Landscape of Decompositional Parameters for ILP | Robert Ganian, Sebastian Ordyniak | Using entirely different techniques, we identify new tractable fragments of ILP by studying structural parameterizations of the constraint matrix within the framework of parameterized complexity. |
| 100 | Abstract Zobrist Hashing: An Efficient Work Distribution Method for Parallel Best-First Search | Yuu Jinnai, Alex Fukunaga | We propose Abstract Zobrist hashing, a new work distribution method for parallel search which reduces node transfers and mitigates communication overhead by using feature projection functions. |
| 101 | Learning to Branch in Mixed Integer Programming | Elias Boutros Khalil, Pierre Le Bodic, Le Song, George Nemhauser, Bistra Dilkina | To address these issues, we propose a machine learning (ML) framework for variable branching in MIP.Our method observes the decisions made by Strong Branching (SB), a time-consuming strategy that produces small search trees, collecting features that characterize the candidate branching variables at each node of the tree. |
| 102 | Local Search for Hard SAT Formulas: The Strength of the Polynomial Law | Sixue Liu, Periklis A. Papakonstantinou | A strong point of our contribution is the conceptual simplicity of our algorithm. |
| 103 | Fast Proximal Linearized Alternating Direction Method of Multiplier with Parallel Splitting | Canyi Lu, Huan Li, Zhouchen Lin, Shuicheng Yan | We propose the Fast Proximal Augmented Lagragian Method (Fast PALM) which achieves the convergence rate O(1/K2), compared with O(1/K) by the traditional PALM. |
| 104 | Combining Bounding Boxes and JPS to Prune Grid Pathfinding | Steve Rabin, Nathan R. Sturtevant | In this paper we look at a specific implementation of the general idea of Geometric Containers, showing that, while it is effective on grid maps, when combined with JPS+ it provides state-of-the-art performance. |
| 105 | Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty | Changkyu Song, Sejong Yoon, Vladimir Pavlovic | We propose new methods to speed up convergence of the Alternating Direction Method of Multipliers (ADMM), a common optimization tool in the context of large scale and distributed learning. |
| 106 | Towards Clause-Learning State Space Search: Learning to Recognize Dead-Ends | Marcel Steinmetz, Joerg Hoffmann | We introduce a state space search method that identifies dead-end states, analyzes the reasons for failure, and learns to avoid similar mistakes in the future. |
| 107 | Implementing Troubleshooting with Batch Repair | Roni Stern, Meir Kalech, Hilla Shinitzky | In this work we propose several algorithms for choosing which batch of components to repair, so as to minimize the overall repair costs. |
| 108 | A Combinatorial Search Perspective on Diverse Solution Generation | Satya Gautam Vadlamudi, Subbarao Kambhampati | In this paper, we take a combinatorial search perspective on generating diverse solutions. |
| 109 | On the Completeness of Best-First Search Variants That Use Random Exploration | Richard Anthony Valenzano, Fan Xie | In this paper, we provide a theoretical justification for this increased robustness by formally analyzing how these algorithms behave on infinite graphs. |
| 110 | DRIMUX: Dynamic Rumor Influence Minimization with User Experience in Social Networks | Biao Wang, Ge Chen, Luoyi Fu, Li Song, Xinbing Wang, Xue Liu | In this paper, we propose a model of dynamic rumor influence minimization with user experience (DRIMUX). |
| 111 | Linearized Alternating Direction Method with Penalization for Nonconvex and Nonsmooth Optimization | Yiyang Wang, Risheng Liu, Xiaoliang Song, Zhixun Su | In this paper, a linearized algorithm with penalization is proposed on the basis of ADM for solving nonconvex and nonsmooth optimization. |
| 112 | Two Efficient Local Search Algorithms for Maximum Weight Clique Problem | Yiyuan Wang, Shaowei Cai, Minghao Yin | This paper introduces two heuristics and develops two local search algorithms for MWCP. |
| 113 | Relaxed Majorization-Minimization for Non-Smooth and Non-Convex Optimization | Chen Xu, Zhouchen Lin, Zhenyu Zhao, Hongbin Zha | We propose a new majorization-minimization (MM) method for non-smooth and non-convex programs, which is general enough to include the existing MM methods. |
| 114 | Submodular Optimization with Routing Constraints | Haifeng Zhang, Yevgeniy Vorobeychik | We propose a generalized cost-benefit (GCB) greedy al- gorithm for our problem, and prove bi-criterion approximation guarantees under significantly weaker assumptions than those in related literature. |
| 115 | Behavioral Experiments in Email Filter Evasion | Liyiming Ke, Bo Li, Yevgeniy Vorobeychik | Despite decades of effort to combat spam, unwanted and even malicious emails, such as phish which aim to deceive recipients into disclosing sensitive information, still routinely find their way into one’s mailbox.To be sure, email filters manage to stop a large fraction of spam emails from ever reaching users, but spammers and phishers have mastered the art of filter evasion, or manipulating the content of email messages to avoid being filtered.We present a unique behavioral experiment designed to study email filter evasion.Our experiment is framed in somewhat broader terms: given the widespread use of machine learning methods for distinguishing spam and non-spam, we investigate how human subjects manipulate a spam template to evade a classification-based filter.We find that adding a small amount of noise to a filter significantly reduces the ability of subjects to evade it, observing that noise does not merely have a short-term impact, but also degrades evasion performance in the longer term.Moreover, we find that greater coverage of an email template by the classifier (filter) features significantly increases the difficulty of evading it.This observation suggests that aggressive feature reduction — a common practice in applied machine learning — can actually facilitate evasion.In addition to the descriptive analysis of behavior, we develop a synthetic model of human evasion behavior which closely matches observed behavior and effectively replicates experimental findings in simulation. |
| 116 | An Oral Exam for Measuring a Dialog System’s Capabilities | David Cohen, Ian Lane | This paper suggests a model and methodology for measuring the breadth and flexibility of a dialog system’s capabilities. |
| 117 | Intelligent Advice Provisioning for Repeated Interaction | Priel Levy, David Sarne | Providing users with suboptimal advice has been reported to be highly advantageous whenever the optimal advice is non-intuitive, hence might not be accepted by the user. |
| 118 | A Deep Choice Model | Makoto Otsuka, Takayuki Osogami | We extend the RBM choice model to a deep choice model (DCM) to deal with the features of items, which are ignored in the RBM choice model. |
| 119 | Personalized Alert Agent for Optimal User Performance | Avraham Shvartzon, Amos Azaria, Sarit Kraus, Claudia V. Goldman, Joachim Meyer, Omer Tsimhoni | %We present a game Based on the solved optimal strategy we present a personalized maintenance agent, which, depending on the value of user’s time, provides alerts to the user when she should perform maintenance. |
| 120 | Minimizing User Involvement for Learning Human Mobility Patterns from Location Traces | Basma Alharbi, Abdulhakim Qahtan, Xiangliang Zhang | In this work, our objective is to minimize human involvement and exploit the power of community in learning `features’ for individuals from their location traces. |
| 121 | Generating CP-Nets Uniformly at Random | Thomas E. Allen, Judy Goldsmith, Hayden Elizabeth Justice, Nicholas Mattei, Kayla Raines | We provide a novel algorithm for provably generating acyclic CP-nets uniformly at random. |
| 122 | Boolean Functions with Ordered Domains in Answer Set Programming | Mario Alviano, Wolfgang Faber, Hannes Strass | In this paper, we develop a new methodology for showing when such checks can be done in deterministic polynomial time. |
| 123 | A Semantical Analysis of Second-Order Propositional Modal Logic | Francesco Belardinelli, Wiebe van der Hoek | This paper is aimed as a contribution to the use of formal modal languages in Artificial Intelligence. |
| 124 | A First-Order Logic of Probability and Only Knowing in Unbounded Domains | Vaishak Belle, Gerhard Lakemeyer, Hector Levesque | In this work, we propose a new general first-order account of probability and only knowing that admits knowledge bases with incomplete and probabilistic specifications. |
| 125 | Explaining Inconsistency-Tolerant Query Answering over Description Logic Knowledge Bases | Meghyn Bienvenu, Camille Bourgaux, François Goasdoué | This paper addresses the problem of explaining why a tuple is a (non-)answer to a query under such semantics. |
| 126 | Automated Verification and Tightening of Failure Propagation Models | Benjamin Bittner, Marco Bozzano, Alessandro Cimatti, Gianni Zampedri | We propose a model checking approach to automatically validate the completeness and tightness of a TFPG for a given infinite-state dynamic system, and a procedure for the automated synthesis of the delay parameters. |
| 127 | A Comparative Study of Ranking-Based Semantics for Abstract Argumentation | Elise Bonzon, Jérôme Delobelle, Sébastien Konieczny, Nicolas Maudet | This is what we propose in this work. |
| 128 | Beyond OWL 2 QL in OBDA: Rewritings and Approximations | Elena Botoeva, Diego Calvanese, Valerio Santarelli, Domenico Fabio Savo, Alessandro Solimando, Guohui Xiao | The aim of this paper is to overcome these limitations of DL-Lite_R, and extend OBDA to more expressive ontology languages, while still leveraging the underlying relational technology for query answering. |
| 129 | SDDs Are Exponentially More Succinct than OBDDs | Simone Bova | We prove that SDDs are more succinct than OBDDs also in theory, by constructing a family of boolean functions where each member has polynomial SDD size but exponential OBDD size. |
| 130 | On the Containment of SPARQL Queries under Entailment Regimes | Melisachew Wudage Chekol | In this paper, we study the containment of SPARQL queries over OWL EL axioms under entailment. |
| 131 | Logical Foundations of Privacy-Preserving Publishing of Linked Data | Bernardo Cuenca Grau, Egor V. Kostylev | In this paper we lay the foundations of privacy-preserving data publishing (PPDP) in the context of Linked Data. |
| 132 | Verifying ConGolog Programs on Bounded Situation Calculus Theories | Giuseppe De Giacomo, Yves Lespérance, Fabio Patrizi, Sebastian Sardina | We address verification of high-level programs over situation calculus action theories that have an infinite object domain, but bounded fluent extensions in each situation. |
| 133 | Qualitative Spatio-Temporal Stream Reasoning with Unobservable Intertemporal Spatial Relations Using Landmarks | Daniel de Leng, Fredrik Heintz | The contribution presented in this paper is two-fold. |
| 134 | Using Decomposition-Parameters for QBF: Mind the Prefix! | Eduart Eiben, Robert Ganian, Sebastian Ordyniak | In this paper we extend the ordinary pathwidth to the QBF-setting by introducing prefix pathwidth, which takes into account the dependencies between variables in a QBF, and show that it leads to an efficient algorithm for QBF. |
| 135 | The Complexity of LTL on Finite Traces: Hard and Easy Fragments | Valeria Fionda, Gianluigi Greco | In this paper we fill this gap and make the following contributions. |
| 136 | SAT-to-SAT: Declarative Extension of SAT Solvers with New Propagators | Tomi Janhunen, Shahab Tasharrofi, Eugenia Ternovska | This paper proposes a novel approach in logic programming that allows (1) logical specification of both the problem itself and its propagators and (2) automatic incorporation of such propagators into the solving process. |
| 137 | Knowledge Graph Completion with Adaptive Sparse Transfer Matrix | Guoliang Ji, Kang Liu, Shizhu He, Jun Zhao | In this paper, we propose a novel approach TranSparse to deal with the two issues. |
| 138 | Locally Adaptive Translation for Knowledge Graph Embedding | Yantao Jia, Yuanzhuo Wang, Hailun Lin, Xiaolong Jin, Xueqi Cheng | In this paper, we propose a locally adaptive translation method for knowledge graph embedding, called TransA, to find the optimal loss function by adaptively determining its margin over different knowledge graphs. |
| 139 | Learning Abductive Reasoning Using Random Examples | Brendan Juba | We consider a new formulation ofabduction in which degrees of “plausibility” of explanations, along with the rules of the domain, arelearned from concrete examples (settings of attributes). |
| 140 | A Model for Learning Description Logic Ontologies Based on Exact Learning | Boris Konev, Ana Ozaki, Frank Wolter | We investigate the problem of learning description logic (DL) ontologies in Angluin et al.’s framework of exact learning via queries posed to an oracle. |
| 141 | Agenda Separability in Judgment Aggregation | Jérôme Lang, Marija Slavkovik, Srdjan Vesic | We propose here a weakening of independence, named agenda separability: a judgment aggregation rule satisfies it if, whenever the agenda is composed of several independent sub-agendas, the resulting collective judgment sets can be computed separately for each sub-agenda and then put together. |
| 142 | Basic Probabilistic Ontological Data Exchange with Existential Rules | Thomas Lukasiewicz, Maria Vanina Martinez, Livia Predoiu, Gerardo I. Simari | We provide an extensive complexity analysis of the problem of deciding the existence of a probabilistic (universal) solution for a given probabilistic source database relative to a (probabilistic) data exchange problem for the different languages considered. |
| 143 | Resistance to Corruption of Strategic Argumentation | Michael J. Maher | Strategic argumentation provides a simple model of disputation. |
| 144 | Causal Explanation Under Indeterminism: A Sampling Approach | Christopher A. Merck, Samantha Kleinberg | Computational methods for causal inference make use of the vast amounts of data collected by individuals to better understand their behavior and improve their health. |
| 145 | ‘Knowing Whether’ in Proper Epistemic Knowledge Bases | Tim Miller, Paolo Felli, Christian Muise, Adrian Pearce, Liz Sonenberg | In this paper, we extend PEKBs to deal with a restricted form of disjunction: ‘knowing whether.’ |
| 146 | Ontology-Mediated Queries for NOSQL Databases | Marie-Laure Mugnier, Marie-Christine Rousset, Federico Ulliana | In this paper, we study the problem of answering ontology-mediated queries on top of key-value stores. |
| 147 | Zero-Suppressed Sentential Decision Diagrams | Masaaki Nishino, Norihito Yasuda, Shin-ichi Minato, Masaaki Nagata | In this paper we introduce an SDD variant, called the Zero-suppressed Sentential Decision Diagram (ZSDD). |
| 148 | Scalable Training of Markov Logic Networks Using Approximate Counting | Somdeb Sarkhel, Deepak Venugopal, Tuan Anh Pham, Parag Singla, Vibhav Gogate | In this paper, we propose principled weight learning algorithms for Markov logic networks that can easily scale to much larger datasets and application domains than existing algorithms. |
| 149 | Metaphysics of Planning Domain Descriptions | Siddharth Srivastava, Stuart Russell, Alessandro Pinto | Unfortunately, as we show in the paper, simple ways of abstracting solvable real-world problems may lead to SLL models that are unsolvable, SLL models whose solutions are incorrect with respect to the real-world problem, or models that are inexpressible in SLLs. |
| 150 | Expressive Recommender Systems through Normalized Nonnegative Models | Cyril J. Stark | We introduce normalized nonnegative models (NNM) for explorative data analysis. |
| 151 | Complexity Results and Algorithms for Extension Enforcement in Abstract Argumentation | Johannes P. Wallner, Andreas Niskanen, Matti Järvisalo | In this work, we focus on the so-called extension enforcement problem in abstract argumentation. |
| 152 | Query Answering with Inconsistent Existential Rules under Stable Model Semantics | Hai Wan, Heng Zhang, Peng Xiao, Haoran Huang, Yan Zhang | In this paper we present a framework of handling inconsistent existential rules under stable model semantics, which is defined by a notion called rule repairs to select maximal components of the existential rules. |
| 153 | Affinity Preserving Quantization for Hashing: A Vector Quantization Approach to Learning Compact Binary Codes | Zhe Wang, Ling-Yu Duan, Tiejun Huang, Gao Wen | Hashing techniques are powerful for approximate nearest neighbour (ANN) search.Existing quantization methods in hashing are all focused on scalar quantization (SQ) which is inferior in utilizing the inherent data distribution.In this paper, we propose a novel vector quantization (VQ) method named affinity preserving quantization (APQ) to improve the quantization quality of projection values, which has significantly boosted the performance of state-of-the-art hashing techniques.In particular, our method incorporates the neighbourhood structure in the pre- and post-projection data space into vector quantization.APQ minimizes the quantization errors of projection values as well as the loss of affinity property of original space.An effective algorithm has been proposed to solve the joint optimization problem in APQ, and the extension to larger binary codes has been resolved by applying product quantization to APQ.Extensive experiments have shown that APQ consistently outperforms the state-of-the-art quantization methods, and has significantly improved the performance of various hashing techniques. |
| 154 | Decidable Verification of Golog Programs over Non-Local Effect Actions | Benjamin Zarrieß, Jens Claßen | In this paper, we introduce two new, more general classes of action theories that allow for context-sensitive, non-local, unbounded effects, i.e. actions that may affect an unbounded number of possibly unnamed objects in a state-dependent fashion. |
| 155 | Mapping Action Language BC to Logic Programs: A Characterization by Postulates | Haodi Zhang, Fangzhen Lin | In this paper, we consider action language BC and show that a standard mapping from BC action descriptions to logic programs can be similarly captured when the action rules in the descriptions do not have consistency conditions. |
| 156 | On the Performance of GoogLeNet and AlexNet Applied to Sketches | Pedro Ballester, Ricardo Matsumura Araujo | Our main goal is to better understand the generalization abilities of these networks and their learned inner representations. |
| 157 | Bayesian Inference of Recursive Sequences of Group Activities from Tracks | Ernesto Brau, Colin Dawson, Alfredo Carrillo, David Sidi, Clayton T. Morrison | We present a probabilistic generative model for inferring a description of coordinated, recursively structured group activities at multiple levels of temporal granularity based on observations of individuals’ trajectories. |
| 158 | Towards Domain Adaptive Vehicle Detection in Satellite Image by Supervised Super-Resolution Transfer | Liujuan Cao, Rongrong Ji, Cheng Wang, Jonathan Li | Towards Domain Adaptive Vehicle Detection in Satellite Image by Supervised Super-Resolution Transfer |
| 159 | Deep Neural Networks for Learning Graph Representations | Shaosheng Cao, Wei Lu, Qiongkai Xu | In this paper, we propose a novel model for learning graph representations, which generates a low-dimensional vector representation for each vertex by capturing the graph structural information. |
| 160 | Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation | Bei Chen, Ning Chen, Jun Zhu, Jiaming Song, Bo Zhang | We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features. |
| 161 | Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks | Hao Chen, Qi Dou, Xi Wang, Jing Qin, Pheng Ann Heng | Traditional methods either employ hand-crafted features to discriminate mitoses from other cells or construct a pixel-wise classifier to label every pixel in a sliding window way. |
| 162 | Deep Contextual Networks for Neuronal Structure Segmentation | Hao Chen, Xiao Juan Qi, Jie Zhi Cheng, Pheng Ann Heng | Deep Contextual Networks for Neuronal Structure Segmentation |
| 163 | Seeing the Unseen Network: Inferring Hidden Social Ties from Respondent-Driven Sampling | Lin Chen, Forrest W. Crawford, Amin Karbasi | Learning about the social structure of hidden and hard-to-reach populations — such as drug users and sex workers — is a major goal of epidemiological and public health research on risk behaviors and disease prevention. |
| 164 | Robust Multi-View Subspace Learning through Dual Low-Rank Decompositions | Zhengming Ding, Yun Fu | To address this, we develop a Robust Multi-view Subspace Learning algorithm (RMSL) through dual low-rank decompositions, which desires to seek a low-dimensional view-invariant subspace for multi-view data. |
| 165 | Graph-without-cut: An Ideal Graph Learning for Image Segmentation | Lianli Gao, Jingkuan Song, Feiping Nie, Fuhao Zou, Nicu Sebe, Heng Tao Shen | In this paper, we propose a novel framework, Graph-Without-Cut (GWC), for learning the similarity graph and image segmentations simultaneously. |
| 166 | MOOCs Meet Measurement Theory: A Topic-Modelling Approach | Jiazhen He, Benjamin I. P. Rubinstein, James Bailey, Rui Zhang, Sandra Milligan, Jeffrey Chan | We aim to develop usable measurement models for highly-instrumented MOOC delivery platforms, by using participation in automatically-extracted online forum topics as items. |
| 167 | Creating Images by Learning Image Semantics Using Vector Space Models | Derrall Heath, Dan Ventura | We present an enhanced semantic model that is used to generate novel images that convey meaning. |
| 168 | Efficient Learning of Timeseries Shapelets | Lu Hou, James T. Kwok, Jacek M. Zurada | In this paper, we take an entirely different approach and reformulate the shapelet discovery task as a numerical optimization problem. |
| 169 | Learning to Appreciate the Aesthetic Effects of Clothing | Jia Jia, Jie Huang, Guangyao Shen, Tao He, Zhiyuan Liu, Huanbo Luan, Chao Yan | In this paper, we formulate this task to a novel three-level framework: visual features(VF) – image-scale space (ISS) – aesthetic words space(AWS). |
| 170 | Consensus Style Centralizing Auto-Encoder for Weak Style Classification | Shuhui Jiang, Ming Shao, Chengcheng Jia, Yun Fu | In this paper, we call these less representative images as weak style images. In addition, we collect a new dataset, Online Shopping, for fashion style classification evaluation, which will be publicly available for vision based fashion style research. |
| 171 | Column Sampling Based Discrete Supervised Hashing | Wang-Cheng Kang, Wu-Jun Li, Zhi-Hua Zhou | In this paper, we propose a novel method, called column sampling based discrete supervised hashing (COSDISH), to directly learn the discrete hashing code from semantic information. |
| 172 | A Framework for Outlier Description Using Constraint Programming | Chia-Tung Kuo, Ian Davidson | In this paper we formulate a related yet understudied problem which we call outlier description. |
| 173 | Random Mixed Field Model for Mixed-Attribute Data Restoration | Qiang Li, Wei Bian, Richard Yi Da Xu, Jane You, Dacheng Tao | In this paper, we develop a new probabilistic model to provide a general and principled method for restoring mixed-attribute data. |
| 174 | Learning with Marginalized Corrupted Features and Labels Together | Yingming Li, Ming Yang, Zenglin Xu, Zhongfei (Mark) Zhang | To improve the generalization performance, in this paper, we propose Regularized Marginalized Cross-View learning (RMCV) by jointly modeling on attribute noise and label noise. |
| 175 | Towards Optimal Binary Code Learning via Ordinal Embedding | Hong Liu, Rongrong Ji, Yongjian Wu, Wei Liu | In this paper, we propose a novel hashing scheme, dubbed Ordinal Embedding Hashing (OEH), which embeds given ordinal relations among data points to learn the ranking-preserving binary codes. |
| 176 | Recognizing Complex Activities by a Probabilistic Interval-Based Model | Li Liu, Li Cheng, Ye Liu, Yongpo Jia, David S. Rosenblum | The framework introduces a latent variable from the Chinese Restaurant Process to explicitly characterize these unique internal configurations of a particular complex activity as a variable number of tables.It can be analytically shown that the resulting interval network satisfies the transitivity property, and as a result, all local temporal dependencies can be retained and are globally consistent.Empirical evaluations on benchmark datasets suggest our approach significantly outperforms the state-of-the-art methods. |
| 177 | Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data | Zitao Liu, Milos Hauskrecht | To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths. |
| 178 | Deep Learning for Algorithm Portfolios | Andrea Loreggia, Yuri Malitsky, Horst Samulowitz, Vijay Saraswat | To alleviate this costly yet crucial step, this paper presents an automated methodology for producing an informative set of features utilizing a deep neural network. |
| 179 | Convolutional Neural Networks over Tree Structures for Programming Language Processing | Lili Mou, Ge Li, Lu Zhang, Tao Wang, Zhi Jin | In this paper, we propose a novel tree-based convolutional neural network (TBCNN) for programming language processing, in which a convolution kernel is designed over programs’ abstract syntax trees to capture structural information. |
| 180 | Learning Tractable Probabilistic Models for Fault Localization | Aniruddh Nath, Pedro Domingos | In this work, we propose Tractable Fault Localization Models (TFLMs) that can be learned from data, and probabilistically infer the location of the bug. |
| 181 | Unsupervised Feature Selection with Structured Graph Optimization | Feiping Nie, Wei Zhu, Xuelong Li | We propose an unsupervised feature selection approach which performs feature selection and local structure learning simultaneously, the similarity matrix thus can be determined adaptively. |
| 182 | Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction | NhatHai Phan, Yue Wang, Xintao Wu, Dejing Dou | In this paper, we concentrate on the auto-encoder, a fundamental component in deep learning, and propose the deep private auto-encoder (dPA). |
| 183 | Privacy-CNH: A Framework to Detect Photo Privacy with Convolutional Neural Network using Hierarchical Features | Lam Tran, Deguang Kong, Hongxia Jin, Ji Liu | In this paper, we propose a new framework called Privacy-CNH that utilizes hierarchical features which include both object and convolutional features in a deep learning model to detect privacy at risk photos. |
| 184 | Drosophila Gene Expression Pattern Annotations via Multi-Instance Biological Relevance Learning | Hua Wang, Cheng Deng, Hao Zhang, Xinbo Gao, Heng Huang | In this paper, we approach the MIL problem from a new perspective using the Class-to-Bag (C2B) distances, which directly assesses the relations between annotation terms and image panels. |
| 185 | Recommending Groups to Users Using User-Group Engagement and Time-Dependent Matrix Factorization | Xin Wang, Roger Donaldson, Christopher Nell, Peter Gorniak, Martin Ester, Jiajun Bu | To tackle these challenges, we adapt existing matrix factorization techniques to learn user-group affinity based on two different implicit engagement metrics: (i) which group-provided content users consume; and (ii) which content users provide to groups. |
| 186 | Instilling Social to Physical: Co-Regularized Heterogeneous Transfer Learning | Ying Wei, Yin Zhu, Cane Wing-ki Leung, Yangqiu Song, Qiang Yang | In order to transfer the knowledge of social and semantic context, we propose a Co-Regularized Heterogeneous Transfer Learning (CoHTL) model, which builds a common semantic space derived from two heterogeneous domains. |
| 187 | Exploiting an Oracle That Reports AUC Scores in Machine Learning Contests | Jacob Whitehill | In this paper we provide proofs-of-concept of how knowledge of the AUC of a set of guesses can be used, in two different kinds of attacks, to improve the accuracy of those guesses. |
| 188 | Efficient Nonparametric Subgraph Detection Using Tree Shaped Priors | Nannan Wu, Feng Chen, Jianxin Li, Baojian Zhou, Naren Ramakrishnan | In this paper, we make a number of contributions to the computational study of NPGS statistics. |
| 189 | Factorization Ranking Model for Move Prediction in the Game of Go | Chenjun Xiao, Martin Müller | In this paper, we investigate the move prediction problem in the game of Go by proposing a new ranking model named Factorization Bradley Terry (FBT) model. |
| 190 | Joint Multi-View Representation Learning and Image Tagging | Zhe Xue, Guorong Li, Qingming Huang | In this paper, we present an optimal predictive subspace learning method which jointly conducts multi-view representation learning and image tagging. |
| 191 | Learning Deep Convolutional Neural Networks for X-Ray Protein Crystallization Image Analysis | Margot Lisa-Jing Yann, Yichuan Tang | In this paper, we present a novel system, CrystalNet, for automatically labeling outcomes of protein crystallization-trial images. |
| 192 | Linear Submodular Bandits with a Knapsack Constraint | Baosheng Yu, Meng Fang, Dacheng Tao | To solve this problem, we propose two greedy algorithms based on a modified UCB rule. |
| 193 | Submodular Asymmetric Feature Selection in Cascade Object Detection | Baosheng Yu, Meng Fang, Dacheng Tao, Jie Yin | In this paper, we improve current feature selection algorithm by addressing both asymmetry and intersection problems. |
| 194 | Semisupervised Autoencoder for Sentiment Analysis | Shuangfei Zhai, Zhongfei (Mark) Zhang | In this paper, we investigate the usage of autoencoders in modeling textual data. |
| 195 | Simultaneous Feature and Sample Reduction for Image-Set Classification | Man Zhang, Ran He, Dong Cao, Zhenan Sun, Tieniu Tan | This paper introduces a joint learning method for image-set classification that simultaneously learns compact binary codes and removes redundant samples. |
| 196 | Collective Noise Contrastive Estimation for Policy Transfer Learning | Weinan Zhang, Ulrich Paquet, Katja Hofmann | We formulate this task as a policy transfer learning problem, and propose a first solution, called collective noise contrastive estimation (collective NCE). |
| 197 | Learning a Hybrid Architecture for Sequence Regression and Annotation | Yizhe Zhang, Ricardo Henao, Lawrence Carin, Jianling Zhong, Alexander Hartemink | In this paper, we present a flexible framework for jointly modeling both latent sequence features and the functional mapping that relates the summary response variables to the hidden state sequence. |
| 198 | Pose-Dependent Low-Rank Embedding for Head Pose Estimation | Handong Zhao, Zhengming Ding, Yun Fu | Head pose estimation via embedding model has beendemonstrated its effectiveness from the recent works.However, most of the previous methods only focuson manifold relationship among poses, while overlookthe underlying global structure among subjects and poses.To build a robust and effective head pose estimator,we propose a novel Pose-dependent Low-Rank Embedding(PLRE) method, which is designed to exploita discriminative subspace to keep within-pose samplesclose while between-pose samples far away. |
| 199 | Cold-Start Heterogeneous-Device Wireless Localization | Vincent W. Zheng, Hong Cao, Shenghua Gao, Aditi Adhikari, Miao Lin, Kevin Chen-Chuan Chang | In this paper, we study a cold-start heterogeneous-devicelocalization problem. |
| 200 | Tracking Idea Flows between Social Groups | Yangxin Zhong, Shixia Liu, Xiting Wang, Jiannan Xiao, Yangqiu Song | To facilitate users in analyzing the flow, we present a method to model the flow behaviors that aims at identifying the lead-lag relationships between word clusters of different user groups. |
| 201 | Fast Hybrid Algorithm for Big Matrix Recovery | Tengfei Zhou, Hui Qian, Zebang Shen, Congfu Xu | In this paper we accelerate the solution procedure by combining non-smooth convex optimization with smooth Riemannian method. |
| 202 | Data Poisoning Attacks against Autoregressive Models | Scott Alfeld, Xiaojin Zhu, Paul Barford | Such models are often trained on data from various sources, some of which may be untrustworthy.An actor in a given market may be incentivised to drive predictions in a certain direction to their own benefit.Prior analyses of intelligent adversaries in a machine-learning context have focused on regression and classification.In this paper we address the non-iid setting of time series forecasting.We consider a forecaster, Bob, using a fixed, known model and a recursive forecasting method.An adversary, Alice, aims to pull Bob’s forecasts toward her desired target series, and may exercise limited influence on the initial values fed into Bob’s model.We consider the class of linear autoregressive models, and a flexible framework of encoding Alice’s desires and constraints.We describe a method of calculating Alice’s optimal attack that is computationally tractable, and empirically demonstrate its effectiveness compared to random and greedy baselines on synthetic and real-world time series data.We conclude by discussing defensive strategies in the face of Alice-like adversaries. |
| 203 | Approximate K-Means++ in Sublinear Time | Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause | We address this problem by proposing a simple and efficient seeding algorithm for K-Means clustering. |
| 204 | Incremental Stochastic Factorization for Online Reinforcement Learning | Andre M. S. Barreto, Rafael L. Beirigo, Joelle Pineau, Doina Precup | In this paper we take a closer look at EMSF. |
| 205 | Increasing the Action Gap: New Operators for Reinforcement Learning | Marc G. Bellemare, Georg Ostrovski, Arthur Guez, Philip S. Thomas, Remi Munos | This paper introduces new optimality-preserving operators on Q-functions. |
| 206 | Decoding Hidden Markov Models Faster Than Viterbi Via Online Matrix-Vector (max, +)-Multiplication | Massimo Cairo, Gabriele Farina, Romeo Rizzi | In this paper, we present a novel algorithm for the maximum a posteriori decoding (MAPD) of time-homogeneous Hidden Markov Models (HMM), improving the worst-case running time of the classical Viterbi algorithm by a logarithmic factor. |
| 207 | Maximum Margin Dirichlet Process Mixtures for Clustering | Gang Chen, Haiying Zhang, Caiming Xiong | In contrast, discriminative classifiers model the conditional probability directly, and have yielded better results than generative classifiers.In this paper, we propose a maximum margin Dirichlet process mixture for clustering, which is different from the traditional DPM for parameter modeling. |
| 208 | Progressive EM for Latent Tree Models and Hierarchical Topic Detection | Peixian Chen, Nevin L. Zhang, Leonard K. M. Poon, Zhourong Chen | In this paper, we propose a method to drastically speed up HLTA using a technique inspired by the advances in the method of moments. |
| 209 | Knowledge Transfer with Interactive Learning of Semantic Relationships | Jonghyun Choi, Sung Ju Hwang, Leonid Sigal, Larry S. Davis | We propose a novel learning framework for object categorization with interactive semantic feedback. |
| 210 | Robustness of Bayesian Pool-Based Active Learning Against Prior Misspecification | Nguyen Viet Cuong, Nan Ye, Wee Sun Lee | We study the robustness of active learning (AL) algorithms against prior misspecification: whether an algorithm achieves similar performance using a perturbed prior as compared to using the true prior. |
| 211 | Learning Step Size Controllers for Robust Neural Network Training | Christian Daniel, Jonathan Taylor, Sebastian Nowozin | This paper investigates algorithms to automatically adapt the learning rate of neural networks (NNs). |
| 212 | Reconstructing Hidden Permutations Using the Average-Precision (AP) Correlation Statistic | Lorenzo De Stefani, Alessandro Epasto, Eli Upfal, Fabio Vandin | We present a generative model for constructing samples from this distribution and prove useful properties of that distribution. |
| 213 | Generalised Brown Clustering and Roll-Up Feature Generation | Leon Derczynski, Sean Chester | In this paper, we present a subtle but profound generalisation of Brown clustering to improve the overall quality by decoupling the number of output classes from the computational active set size. |
| 214 | Random Composite Forests | Giulia DeSalvo, Mehryar Mohri | We introduce a broad family of decision trees, Composite Trees, whose leaf classifiers are selected out of a hypothesis set composed of p subfamilies with different complexities. |
| 215 | The Ostomachion Process | Xuhui Fan, Bin Li, Yi Wang, Yang Wang, Fang Chen | To overcome this limitation, we propose the Ostomachion process (OP), which relaxes the cutting direction by allowing for oblique cuts. |
| 216 | Indexable Probabilistic Matrix Factorization for Maximum Inner Product Search | Marco Fraccaro, Ulrich Paquet, Ole Winther | We introduce Indexable Probabilistic Matrix Factorization (IPMF) to shift the traditional post-processing complexity into the training phase of the model. |
| 217 | Fast Lasso Algorithm via Selective Coordinate Descent | Yasuhiro Fujiwara, Yasutoshi Ida, Hiroaki Shiokawa, Sotetsu Iwamura | This paper proposes Sling, a fast approach to the lasso. |
| 218 | Group and Graph Joint Sparsity for Linked Data Classification | Longwen Gao, Shuigeng Zhou | In this paper, motivated by effectively classifying linked data (e.g. Web pages, tweets, articles with references, and biological network data) where a group structure exists over the whole dataset and links exist between specific samples, we propose a joint sparse representation model that combines group sparsity and graph sparsity, to select a small number of connected components from the graph of linked samples, meanwhile promoting the sparsity of edges that link samples from different groups in each connected component. |
| 219 | Risk Minimization in the Presence of Label Noise | Wei Gao, Lu Wang, Yu-Feng li, Zhi-Hua Zhou | In this paper, we present new Bernstein concentration inequalities depending only on the first moments of random matrices, whereas previous Bernstein inequalities are heavily relevant to the first and second moments. |
| 220 | Decentralized Approximate Bayesian Inference for Distributed Sensor Network | Behnam Gholami, Sejong Yoon, Vladimir Pavlovic | In this paper we propose a framework for decentralized Bayesian learning using Bregman Alternating Direction Method of Multipliers (B-ADMM). |
| 221 | Assumed Density Filtering Methods for Learning Bayesian Neural Networks | Soumya Ghosh, Francesco Maria Delle Fave, Jonathan Yedidia | Here, we study algorithms that utilize recent advances in Bayesian inference to efficiently learn distributions over network weights. |
| 222 | Extending the Modelling Capacity of Gaussian Conditional Random Fields while Learning Faster | Jesse Glass, Mohamed Ghalwash, Milan Vukicevic, Zoran Obradovic | Extending the Modelling Capacity of Gaussian Conditional Random Fields while Learning Faster |
| 223 | Uncertainty Propagation in Long-Term Structured Regression on Evolving Networks | Djordje Gligorijevic, Jelena Stojanovic, Zoran Obradovic | To address this problem, an effective novel iterative method is developed for Gaussian structured learning models in this study for propagating uncertainty in temporal graphs by modeling noisy inputs. |
| 224 | Teaching-to-Learn and Learning-to-Teach for Multi-label Propagation | Chen Gong, Dacheng Tao, Jie Yang, Wei Liu | Existing methods ignore the specific propagation difficulty of different unlabeled examples and conduct the propagationin an imperfect sequence, leading to the error-prone classification of some difficult examples with uncertain labels. |
| 225 | Discriminative Analysis Dictionary Learning | Jun Guo, Yanqing Guo, Xiangwei Kong, Man Zhang, Ran He | This paper presents a novel DL method, namely Discriminative Analysis Dictionary Learning (DADL), to improve the classification performance of ADL. |
| 226 | Active Learning with Cross-Class Knowledge Transfer | Yuchen Guo, Guiguang Ding, Yuqi Wang, Xiaoming Jin | In this paper, we focus on a more challenging cross-class setting where the class labels are totally different in two domains but related to each other in an intermediary attribute space, which is barely investigated before. |
| 227 | Generalized Emphatic Temporal Difference Learning: Bias-Variance Analysis | Assaf Hallak, Aviv Tamar, Remi Munos, Shie Mannor | We propose a generalization of the recently introduced emphatic temporal differences (ETD) algorithm, which encompasses the original ETD(λ), as well as several other off-policy evaluation algorithms as special cases. |
| 228 | Multi-Stage Multi-Task Learning with Reduced Rank | Lei Han, Yu Zhang | To address the issue, we propose a Reduced rAnk MUlti-Stage multi-tAsk learning (RAMUSA) method based on the recently proposed capped norms. |
| 229 | Reduction Techniques for Graph-Based Convex Clustering | Lei Han, Yu Zhang | In this paper, we develop efficient graph reduction techniques for the GCC model to eliminate edges, each of which corresponds to two data points from the same cluster, without solving the optimization problem in the GCC method, leading to improved computational efficiency. |
| 230 | SAND: Semi-Supervised Adaptive Novel Class Detection and Classification over Data Stream | Ahsanul Haque, Latifur Khan, Michael Baron | We propose an efficient semi-supervised framework in this paper which uses change detection on classifier confidence to detect concept drifts, and to determine chunk boundaries dynamically. |
| 231 | Flattening the Density Gradient for Eliminating Spatial Centrality to Reduce Hubness | Kazuo Hara, Ikumi Suzuki, Kei Kobayashi, Kenji Fukumizu, Milos Radovanovic | As described in this paper, we propose a solution for the hubness problem when Euclidean distance is considered. |
| 232 | Discriminative Vanishing Component Analysis | Chenping Hou, Feiping Nie, Dacheng Tao | Based on the analysis above, we proposed a novel Discriminative Vanishing Component Analysis (DVCA) approach. |
| 233 | Common and Discriminative Subspace Kernel-Based Multiblock Tensor Partial Least Squares Regression | Ming Hou, Qibin Zhao, Brahim Chaib-draa, Andrzej Cichocki | In this work, we introduce a new generalized nonlinear tensor regression framework called kernel-based multiblock tensor partial least squares (KMTPLS) for predicting a set of dependent tensor blocks from a set of independent tensor blocks through the extraction of a small number of common and discriminative latent components. |
| 234 | Multi-Label Manifold Learning | Peng Hou, Xin Geng, Min-Ling Zhang | Based on this, we propose a novel method called ML2, i.e., Multi-Label Manifold Learning, to reconstruct and exploit the label manifold. |
| 235 | Optimal Discrete Matrix Completion | Zhouyuan Huo, Ji Liu, Heng Huang | In this paper, we propose a novel optimal discrete matrix completion model, which is able to learn optimal thresholds automatically and also guarantees an exact low-rank structure of the target matrix. |
| 236 | Conservativeness of Untied Auto-Encoders | Daniel Jiwoong Im, Mohamed Ishmael Belghazi, Roland Memisevic | We discuss necessary and sufficient conditions for an auto-encoder to define a conservative vector field, in which case it is associated with anenergy function akin to the unnormalized log-probability of the data.We show that the conditions for conservativeness are more general than for encoder and decoder weights to be the same (“tied weights”), and thatthey also depend on the form of the hidden unit activation functions.Moreover, we show that contractive training criteria, such as denoising, enforces these conditions locally.Based on these observations, we show how we can use auto-encoders to extract the conservative component of a vector field. |
| 237 | Infinite Plaid Models for Infinite Bi-Clustering | Katsuhiko Ishiguro, Issei Sato, Masahiro Nakano, Akisato Kimura, Naonori Ueda | We propose a probabilistic model for non-exhaustive and overlapping (NEO) bi-clustering. |
| 238 | Improving Predictive State Representations via Gradient Descent | Nan Jiang, Alex Kulesza, Satinder Singh | In practice, however, model mismatch is inevitable and while spectral learning remains appealingly fast and simple it may fail to find optimal models. |
| 239 | A Probabilistic Approach to Knowledge Translation | Shangpu Jiang, Daniel Lowd, Dejing Dou | In this paper, we focus on a novel knowledge reuse scenario where the knowledge in the source schema needs to be translated to a semantically heterogeneous target schema. |
| 240 | The l2,1-Norm Stacked Robust Autoencoders for Domain Adaptation | Wenhao Jiang, Hongchang Gao, Fu-lai Chung, Heng Huang | In this paper, a deep learning method for domain adaptation calledl2,1-norm stacked robust autoencoders (l2,1-SRA) is proposed to learn useful representations for domain adaptation tasks. |
| 241 | Wishart Mechanism for Differentially Private Principal Components Analysis | Wuxuan Jiang, Cong Xie, Zhihua Zhang | We propose a new input perturbation mechanism for publishing a covariance matrix to achieve (epsilon,0)-differential privacy. |
| 242 | Deep Learning with S-Shaped Rectified Linear Activation Units | Xiaojie Jin, Chunyan Xu, Jiashi Feng, Yunchao Wei, Junjun Xiong, Shuicheng Yan | In this paper, we propose a novel S-shaped rectifiedlinear activation unit (SReLU) to learn both convexand non-convex functions, imitating the multiple function forms given by the two fundamental laws, namely the Webner-Fechner law and the Stevens law, in psychophysics and neural sciences. |
| 243 | Delay-Tolerant Online Convex Optimization: Unified Analysis and Adaptive-Gradient Algorithms | Pooria Joulani, Andras Gyorgy, Csaba Szepesvari | We present a unified, black-box-style method for developing and analyzing online convex optimization (OCO) algorithms for full-information online learning in delayed-feedback environments. |
| 244 | Shakeout: A New Regularized Deep Neural Network Training Scheme | Guoliang Kang, Jun Li, Dacheng Tao | In this paper, we present a new training scheme: Shakeout. |
| 245 | Bounded Optimal Exploration in MDP | Kenji Kawaguchi | In this paper, we relax the PAC-MDP conditions to reconcile theoretically driven exploration methods and practical needs. |
| 246 | Uncorrelated Group LASSO | Deguang Kong, Ji Liu, Bo Liu, Xuan Bao | To solve the generic exclusive groupl2,1-norm regularized problems, we propose an efficient iterative re-weighting algorithm and provide a rigorous convergence analysis. |
| 247 | Learning Future Classifiers without Additional Data | Atsutoshi Kumagai, Tomoharu Iwata | We propose probabilistic models for predicting future classifiers given labeled data with timestamps collected until the current time. |
| 248 | Compressed Conditional Mean Embeddings for Model-Based Reinforcement Learning | Guy Lever, John Shawe-Taylor, Ronnie Stafford, Csaba Szepesvari | We present a model-based approach to solving Markov decision processes (MDPs) in which the system dynamics are learned using conditional mean embeddings (CMEs). |
| 249 | Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks | Chunyuan Li, Changyou Chen, David Carlson, Lawrence Carin | Here, we propose combining adaptive preconditioners with SGLD. |
| 250 | High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep Models | Chunyuan Li, Changyou Chen, Kai Fan, Lawrence Carin | To this end, we propose use of an efficient symmetric splitting integrator in mSGNHT, instead of the traditional Euler integrator. |
| 251 | Multi-Objective Self-Paced Learning | Hao Li, Maoguo Gong, Deyu Meng, Qiguang Miao | Multi-Objective Self-Paced Learning |
| 252 | Scalable Sequential Spectral Clustering | Yeqing Li, Junzhou Huang, Wei Liu | In order to overcome this issue, we propose a novel sequential SC algorithm for tackling large-scale clustering with limited computational resources, \textit{e.g.}, memory. |
| 253 | Towards Safe Semi-Supervised Learning for Multivariate Performance Measures | Yu-Feng Li, James T. Kwok, Zhi-Hua Zhou | To alleviate this problem, we propose in this paper the UMVP (safe semi-sUpervised learning for MultiVariate Performance measure) method, because of the need of various performance measures in practical tasks. |
| 254 | Accelerating Random Kaczmarz Algorithm Based on Clustering Information | Yujun Li, Kaichun Mo, Haishan Ye | During each updating step, Kaczmarz chooses a hyperplane based on an individual equation and projects the current estimate for the exact solution onto that space to get a new estimate.Many vairants of Kaczmarz algorithms are proposed on how to choose better hyperplanes.Using the property of randomly sampled data in high-dimensional space,we propose an accelerated algorithm based on clustering information to improve block Kaczmarz and Kaczmarz via Johnson-Lindenstrauss lemma. |
| 255 | Fast and Accurate Refined Nyström-Based Kernel SVM | Zhe Li, Tianbao Yang, Lijun Zhang, Rong Jin | In this paper, we focus on improving the performance of the Nyström based kernel SVM. |
| 256 | How Important Is Weight Symmetry in Backpropagation? | Qianli Liao, Joel Z. Leibo, Tomaso Poggio | Using 15 different classification datasets, we systematically investigate to what extent BP really depends on weight symmetry. |
| 257 | Re-Active Learning: Active Learning with Relabeling | Christopher H. Lin, M Mausam, Daniel S. Weld | We show how traditional active learning methods perform poorly at re-active learning, present new algorithms designed for this important problem, formally characterize their behavior, and empirically show that our methods effectively make this tradeoff. |
| 258 | Interaction Point Processes via Infinite Branching Model | Peng Lin, Bang Zhang, Ting Guo, Yang Wang, Fang Chen | In this paper, we propose the infinite branching model (IBM), a Bayesian statistical model that can generalize and extend some popular IPPs, e.g., Hawkes process (Hawkes 1971; Hawkes and Oakes 1974). |
| 259 | Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond | Chun Kai Ling, Kian Hsiang Low, Patrick Jaillet | A key contribution of our work here thus lies in exploiting the Lipschitz continuity of the reward functions to solve for a nonmyopic adaptive epsilon-optimal GPP (epsilon-GPP) policy. |
| 260 | Online ARIMA Algorithms for Time Series Prediction | Chenghao Liu, Steven C.H. Hoi, Peilin Zhao, Jianling Sun | In this paper, we propose online learning algorithms for estimating ARIMA models under relaxed assumptions on the noise terms, which is suitable to a wider range of applications and enjoys high computational efficiency. |
| 261 | Consensus Guided Unsupervised Feature Selection | Hongfu Liu, Ming Shao, Yun Fu | Differently, in this work we introduce consensus clustering for pseudo labeling, which gets rid of expensive eigen-decomposition and provides better clustering accuracy with high robustness. |
| 262 | Sparse Perceptron Decision Tree for Millions of Dimensions | Weiwei Liu, Ivor W. Tsang | Following our analysis, we introduce the notion of sparse perceptron decision node (SPDN) with a budget constraint on the weight coefficients, and propose a sparse perceptron decision tree (SPDT) algorithm to achieve nonlinear prediction performance. |
| 263 | Multiple Kernel | Xinwang Liu, Yong Dou, Jianping Yin, Lei Wang, En Zhu | To address this issue, this paper proposes an MKKM clustering with a novel, effective matrix-induced regularization to reduce such redundancy and enhance the diversity of the selected kernels. |
| 264 | Finding One’s Best Crowd: Online Learning By Exploiting Source Similarity | Yang Liu, Mingyan Liu | We further relax both (1) and (2) and present a cost-efficient algorithm that identifies a best crowd from a potentially large set of data sources in terms of both classifier performance and data acquisition cost. |
| 265 | Learning FRAME Models Using CNN Filters | Yang Lu, Song-Chun Zhu, Ying Nian Wu | In this conceptual paper, we study the generative perspective of the discriminative CNN. |
| 266 | Sparse Latent Space Policy Search | Kevin Sebastian Luck, Joni Pajarinen, Erik Berger, Ville Kyrki, Heni Ben Amor | We introduce a reinforcement learning method for sample-efficient policy search that exploits correlations between control variables. |
| 267 | Expected Tensor Decomposition with Stochastic Gradient Descent | Takanori Maehara, Kohei Hayashi, Ken-ichi Kawarabayashi | In this study, we investigate expected CP decomposition — a special case of CP decomposition in which a tensor to be decomposed is given as the sum or average of tensor samplesX(t) fort = 1,…,T. To determine this decomposition, we develope stochastic-gradient-descent-type algorithms with four appealing features: efficient memory use, ability to work in an online setting, robustness of parameter tuning, and simplicity. |
| 268 | Offline Evaluation of Online Reinforcement Learning Algorithms | Travis Mandel, Yun-En Liu, Emma Brunskill, Zoran Popović | In this work, we develop three new evaluation approaches which guarantee that, given some history, algorithms are fed samples from the distribution that they would have encountered if they were run online. |
| 269 | Reinforcement Learning with Parameterized Actions | Warwick Masson, Pravesh Ranchod, George Konidaris | We introduce a model-free algorithm for learning in Markov decision processes with parameterized actions—discrete actions with continuous parameters. |
| 270 | Fixed-Rank Supervised Metric Learning on Riemannian Manifold | Yadong Mu | In this paper, we tackle low-rank metric learning by enforcing fixed-rank constraint on the matrixW. |
| 271 | All-in Text: Learning Document, Label, and Word Representations Jointly | Jinseok Nam, Eneldo Loza Mencía, Johannes Fürnkranz | In this paper, we investigate an approach for embedding documents and labels into a joint space while sharing word representations between documents and labels. |
| 272 | Holographic Embeddings of Knowledge Graphs | Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio | In this work, we propose holographic embeddings (HolE) to learn compositional vector space representations of entire knowledge graphs. |
| 273 | New l1-Norm Relaxations and Optimizations for Graph Clustering | Feiping Nie, Hua Wang, Cheng Deng, Xinbo Gao, Xuelong Li, Heng Huang | In this paper, we propose a new optimization algorithm to solve this difficult non-smooth ratio minimization problem. |
| 274 | The Constrained Laplacian Rank Algorithm for Graph-Based Clustering | Feiping Nie, Xiaoqian Wang, Michael I. Jordan, Heng Huang | We derive optimization algorithms to solve these objectives. |
| 275 | Efficient PAC-Optimal Exploration in Concurrent, Continuous State MDPs with Delayed Updates | Jason Pazis, Ronald Parr | We present a new, efficient PAC optimal exploration algorithm that is able to explore in multiple, continuous or discrete state MDPs simultaneously. |
| 276 | Viral Clustering: A Robust Method to Extract Structures in Heterogeneous Datasets | Vahan Petrosyan, Alexandre Proutiere | In this paper, we develop Viral Clustering (VC), a simple algorithm that jointly estimates the number of clusters and outputs clusters. |
| 277 | Inverse Reinforcement Learning through Policy Gradient Minimization | Matteo Pirotta, Marcello Restelli | Inverse Reinforcement Learning (IRL) deals with the problem of recovering the reward function optimized by an expert given a set of demonstrations of the expert’s policy.Most IRL algorithms need to repeatedly compute the optimal policy for different reward functions.This paper proposes a new IRL approach that allows to recover the reward function without the need of solving any “direct” RL problem.The idea is to find the reward function that minimizes the gradient of a parameterized representation of the expert’s policy.In particular, when the reward function can be represented as a linear combination of some basis functions, we will show that the aforementioned optimization problem can be efficiently solved.We present an empirical evaluation of the proposed approach on a multidimensional version of the Linear-Quadratic Regulator (LQR) both in the case where the parameters of the expert’s policy are known and in the (more realistic) case where the parameters of the expert’s policy need to be inferred from the expert’s demonstrations.Finally, the algorithm is compared against the state-of-the-art on the mountain car domain, where the expert’s policy is unknown. |
| 278 | Scaling Simultaneous Optimistic Optimization for High-Dimensional Non-Convex Functions with Low Effective Dimensions | Hong Qian, Yang Yu | We prove that the simple regret of RESOO depends only on the effective dimension of the problem, while that of SOO depends on the dimension of the solution space. |
| 279 | Selecting Near-Optimal Learners via Incremental Data Allocation | Ashish Sabharwal, Horst Samulowitz, Gerald Tesauro | Inspired by the principle of “optimism under uncertainty,” we propose an innovative strategy, Data Allocation using Upper Bounds (DAUB), which robustly achieves these objectives across a variety of real-world datasets. |
| 280 | Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization | Fanhua Shang, Yuanyuan Liu, James Cheng | Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization |
| 281 | Spectral Bisection Tree Guided Deep Adaptive Exemplar Autoencoder for Unsupervised Domain Adaptation | Ming Shao, Zhengming Ding, Handong Zhao, Yun Fu | In this paper, we extend the deep representation learning to domain adaptation scenario, and propose a novel deep model called “Deep Adaptive Exemplar AutoEncoder (DAE$^2$)”. |
| 282 | Metric Learning for Ordinal Data | Yuan Shi, Wenzhe Li, Fei Sha | In this paper, we present a novel metric learning algorithm that takes into consideration the nature of ordinal data. |
| 283 | Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization | Adish Singla, Sebastian Tschiatschek, Andreas Krause | We address the problem of maximizing an unknown submodular function that can only be accessed via noisy evaluations. |
| 284 | Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization | Yang Song, Jun Zhu | We fill this gap by presenting a novel Bayesian matrix completion method based on spectral regularization. |
| 285 | Marginalized Continuous Time Bayesian Networks for Network Reconstruction from Incomplete Observations | Lukas Studer, Loic Paulevé, Christoph Zechner, Matthias Reumann, María Rodríguez Martínez, Heinz Koeppl | We therefore focus on the structure learning problem and present a way to analytically marginalize the Markov chain underlying the CTBN model with respect its parameters. |
| 286 | Return of Frustratingly Easy Domain Adaptation | Baochen Sun, Jiashi Feng, Kate Saenko | We propose a simple, effective, and efficient method for unsupervised domain adaptation called CORrelation ALignment (CORAL). |
| 287 | On the Depth of Deep Neural Networks: A Theoretical View | Shizhao Sun, Wei Chen, Liwei Wang, Xiaoguang Liu, Tie-Yan Liu | Our experiments show that in this way, we achieve significantly better test performance. |
| 288 | Linear-Time Learning on Distributions with Approximate Kernel Embeddings | Dougal J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff Schneider | We provide an analysis of the approximation error in using our proposed random features, and show empirically the quality of our approximation both in estimating a Gram matrix and in solving learning tasks in real-world and synthetic data. |
| 289 | Learning Sparse Confidence-Weighted Classifier on Very High Dimensional Data | Mingkui Tan, Yan Yan, Li Wang, Anton Van Den Hengel, Ivor W. Tsang, Qinfeng (Javen) Shi | Learning Sparse Confidence-Weighted Classifier on Very High Dimensional Data |
| 290 | Algorithms for Differentially Private Multi-Armed Bandits | Aristide C. Y. Tossou, Christos Dimitrakakis | We present differentially private algorithms for the stochastic Multi-Armed Bandit (MAB) problem. |
| 291 | Deep Reinforcement Learning with Double Q-Learning | Hado van Hasselt, Arthur Guez, David Silver | In this paper, we answer all these questions affirmatively. |
| 292 | Online Instrumental Variable Regression with Applications to Online Linear System Identification | Arun Venkatraman, Wen Sun, Martial Hebert, J. Andrew Bagnell, Byron Boots | In this work, we develop Online Instrumental Variable Regression (OIVR), an algorithm that is capable of updating the learned estimator with streaming data. |
| 293 | The Hidden Convexity of Spectral Clustering | James Voss, Mikhail Belkin, Luis Rademacher | In this paper we propose a new class of algorithms for multiway spectral clustering based on optimization of a certain “contrast function” over the unit sphere. |
| 294 | Multitask Generalized Eigenvalue Program | Boyu Wang, Joelle Pineau, Borja Balle | We present a novel multitask learning framework called multitask generalized eigenvalue program (MTGEP), which jointly solves multiple related generalized eigenvalue problems (GEPs). |
| 295 | Product Grassmann Manifold Representation and Its LRR Models | Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Baocai Yin | In this paper, we intend to cluster complex high dimensional data with multiple varying factors. |
| 296 | Text Classification with Heterogeneous Information Network Kernels | Chenguang Wang, Yangqiu Song, Haoran Li, Ming Zhang, Jiawei Han | This paper presents a novel text as network classification framework, which introduces 1) a structured and typed heterogeneous information networks (HINs) representation of texts, and 2) a meta-path based approach to link texts. |
| 297 | Semi-Supervised Dictionary Learning via Structural Sparse Preserving | Di Wang, Xiaoqin Zhang, Mingyu Fan, Xiuzi Ye | In this paper, we present a novel semi- supervised dictionary learning method which utilizes the structural sparse relationships between the labeled and unlabeled samples. |
| 298 | Relational Knowledge Transfer for Zero-Shot Learning | Donghui Wang, Yanan Li, Yuetan Lin, Yueting Zhuang | In this paper, we reveal a novel relational knowledge transfer (RKT) mechanism for ZSL, which is simple, generic and effective. |
| 299 | Optimizing Multivariate Performance Measures from Multi-View Data | Jim Jing-Yan Wang, Ivor Wai-Hung Tsang, Xin Gao | To fill this gap, in this paper, we propose the problem of optimizing multivariate performance measures from multi-view data, and an effective method to solve it. |
| 300 | An Efficient Time Series Subsequence Pattern Mining and Prediction Framework with an Application to Respiratory Motion Prediction | Shouyi Wang, Kinming Kam, Cao Xiao, Stephen Bowen, Wanpracha Art Chaovalitwongse | To address these problems, we propose a flexible time series pattern representation and selection framework, called the orthogonalpolynomial-based variant-nearest-neighbor (OPVNN) approach. |
| 301 | Co-Regularized PLSA for Multi-Modal Learning | Xin Wang, MingChing Chang, Yiming Ying, Siwei Lyu | In this work, we study co-regularized PLSA(coPLSA) as an efficient solution to probabilistic topic analysis of multi-modal data. |
| 302 | Toward a Better Understanding of Deep Neural Network Based Acoustic Modelling: An Empirical Investigation | Xingfu Wang, Lin Wang, Jing Chen, Litao Wu | Our work aims to uncover which variations among baseline systems are most relevant for automatic speech recognition (ASR) performance via a series of systematic tests on the limits of the major architectural choices.By holding all the other components fixed, we are able to explore the design and training decisions without being confounded by the other influencing factors. |
| 303 | Noise-Adaptive Margin-Based Active Learning and Lower Bounds under Tsybakov Noise Condition | Yining Wang, Aarti Singh | We present a simple noise-robust margin-based active learn-ing algorithm to find homogeneous (passing the origin) linearseparators and analyze its error convergence when labels arecorrupted by noise. |
| 304 | Learning by Transferring from Unsupervised Universal Sources | Yu-Xiong Wang, Martial Hebert | In this paper, we address this largely-overlooked yet fundamental source problem by both introducing a systematic scheme for generating universal source hypotheses and proposing a principled, scalable approach to automatically tuning the transfer process. |
| 305 | Learning Deep ℓ | Zhangyang Wang, Qing Ling, Thomas S. Huang | We study the ℓ0 sparse approximation problem with the tool of deep learning, by proposing Deep ℓ0 Encoders. |
| 306 | Adaptive Normalized Risk-Averting Training for Deep Neural Networks | Zhiguang Wang, Tim Oates, James Lo | This paper proposes a set of new error criteria and a learning approach, called Adaptive Normalized Risk-Averting Training (ANRAT) to attack the non-convex optimization problem in training deep neural networks without pretraining. |
| 307 | Nonlinear Feature Extraction with Max-Margin Data Shifting | Jianqiao Wangni, Ning Chen | In this paper, we present a simple and efficient method, named max-margin data shifting (MMDS), to process the data before feature extraction. |
| 308 | Unsupervised Feature Selection on Networks: A Generative View | Xiaokai Wei, Bokai Cao, Philip S. Yu | In this paper, we investigate the problem of unsupervised feature selection on networks. |
| 309 | Model-Free Preference-Based Reinforcement Learning | Christian Wirth, Johannes Fürnkranz, Gerhard Neumann | In this paper, we integrate preference-based estimation of the reward function into a model-free reinforcement learning (RL) algorithm, resulting in a model-free PBRL algorithm. |
| 310 | Constrained Submodular Minimization for Missing Labels and Class Imbalance in Multi-label Learning | Baoyuan Wu, Siwei Lyu, Bernard Ghanem | In this work, we propose a new method to handle these two challenges simultaneously. |
| 311 | Representing Sets of Instances for Visual Recognition | Jianxin Wu, Bin-Bin Gao, Guoqing Liu | The proposed D3 method effectively compares two sets as two distributions, and proposes a directional total variation distance (DTVD) to measure their dissimilarity. |
| 312 | Robust Semi-Supervised Learning through Label Aggregation | Yan Yan, Zhongwen Xu, Ivor W. Tsang, Guodong Long, Yi Yang | To address these two challenges, in this paper, we propose an efficient RObust Semi-Supervised Ensemble Learning (ROSSEL) method, which generates pseudo-labels for unlabeled data using a set of weak annotators, and combines them to approximate the ground-truth labels to assist semi-supervised learning. |
| 313 | Analysis-Synthesis Dictionary Learning for Universality-Particularity Representation Based Classification | Meng Yang, Weiyang Liu, Weixin Luo, Linlin Shen | In this paper, we proposed a novel model of analysis-synthesis dictionary learning for universality-particularity (ASDL-UP) representation based classification. |
| 314 | Efficient Average Reward Reinforcement Learning Using Constant Shifting Values | Shangdong Yang, Yang Gao, Bo An, Hao Wang, Xingguo Chen | In this paper, a novel model-free algorithm is proposed, which makes use of constant shifting values (CSVs) estimated from prior knowledge. |
| 315 | Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach | Shuo Yang, Tushar Khot, Kristian Kersting, Sriraam Natarajan | We therefore develop the first relational representation called Relational Continuous-Time Bayesian Networks (RCTBNs) that can address this challenge. |
| 316 | Instance Specific Metric Subspace Learning: A Bayesian Approach | Han-Jia Ye, De-Chuan Zhan, Yuan Jiang | In this paper, we propose isMets (Instance Specific METric Subspace) framework which can automatically span the whole metric space in a generative manner and is able to inductively learn a specific metric subspace for each instance via inferring the expectation over the metric bases in a Bayesian manner. |
| 317 | Scalable Completion of Nonnegative Matrix with Separable Structure | Xiyu Yu, Wei Bian, Dacheng Tao | In this paper, we present a new model for matrix completion, motivated by the separability assumption of nonnegative matrices from the recent literature of matrix factorisations: there exists a set of columns of the matrix such that the resting columns can be represented by their convex combinations. |
| 318 | Derivative-Free Optimization via Classification | Yang Yu, Hong Qian, Yi-Qi Hu | Following the critical factors, we propose the randomized coordinate shrinking classification algorithm to learn the model, forming the RACOS algorithm, for optimization in continuous and discrete domains. |
| 319 | On Order-Constrained Transitive Distance Clustering | Zhiding Yu, Weiyang Liu, Wenbo Liu, Yingzhen Yang, Ming Li, B. V. K. Vijaya Kumar | We therefore propose a fast approximation framework, using random samplings to generate multiple diversified TD matrices and a pooling to output the final approximated OCTD matrix. |
| 320 | A Proximal Alternating Direction Method for Semi-Definite Rank Minimization | Ganzhao Yuan, Bernard Ghanem | In this paper, we propose a proximal Alternating Direction Method (ADM) for the well-known semi-definite rank regularized minimization problem. |
| 321 | Learning Expected Hitting Time Distance | De-Chuan Zhan, Peng Hu, Zui Chu, Zhi-Hua Zhou | In this work, we define a non-Mahalanobis distance for histogram features, via Expected Hitting Time (EHT) of Markov Chain, which implicitly considers the high-order feature relationships between different histogram features. |
| 322 | Stochastic Optimization for Kernel PCA | Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou | To address this limitation, we utilize techniques from stochastic optimization to solve kernel PCA with linear space and time complexities per iteration. |
| 323 | Asynchronous Distributed Semi-Stochastic Gradient Optimization | Ruiliang Zhang, Shuai Zheng, James T. Kwok | In this paper, we combine their merits by proposing a fast distributed asynchronous SGD-based algorithm with variance reduction. |
| 324 | An Alternating Proximal Splitting Method with Global Convergence for Nonconvex Structured Sparsity Optimization | Shubao Zhang, Hui Qian, Xiaojin Gong | In this paper, we propose a splitting method for solving nonconvex structured sparsity optimization problems. |
| 325 | Accelerated Sparse Linear Regression via Random Projection | Weizhong Zhang, Lijun Zhang, Rong Jin, Deng Cai, Xiaofei He | In this paper, we present an accelerated numerical method based on random projection for sparse linear regression. |
| 326 | Large-Scale Graph-Based Semi-Supervised Learning via Tree Laplacian Solver | Yan-Ming Zhang, Xu-Yao Zhang, Xiao-Tong Yuan, Cheng-Lin Liu | In this paper, we address this scalability issue by proposing a method that approximately solves the quadratic objective in nearly linear time. |
| 327 | Near-Optimal Active Learning of Multi-Output Gaussian Processes | Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan Kankanhalli | This paper addresses the problem of active learning of a multi-output Gaussian process (MOGP) model representing multiple types of coexisting correlated environmental phenomena. |
| 328 | Multi-Domain Active Learning for Recommendation | Zihan Zhang, Xiaoming Jin, Lianghao Li, Guiguang Ding, Qiang Yang | In this paper, we propose an active learning strategy for recommendation to alleviate the data sparsity in a multi-domain scenario. |
| 329 | On the Differential Privacy of Bayesian Inference | Zuhe Zhang, Benjamin I. P. Rubinstein, Christos Dimitrakakis | Our main contributions are four different algorithms for private Bayesian inference on probabilistic graphical models. |
| 330 | A Scalable and Extensible Framework for Superposition-Structured Models | Shenjian Zhao, Cong Xie, Zhihua Zhang | Employing the smoothed conic dual approach with the LBFGS updating formula, we propose a scalable and extensible proximal quasi-Newton (SEP-QN) framework. |
| 331 | Fast Asynchronous Parallel Stochastic Gradient Descent: A Lock-Free Approach with Convergence Guarantee | Shen-Yi Zhao, Wu-Jun Li | In this paper, we propose a fast asynchronous parallel SGD method, called AsySVRG, by designing an asynchronous strategy to parallelize the recently proposed SGD variant called stochastic variance reduced gradient (SVRG). |
| 332 | DinTucker: Scaling Up Gaussian Process Models on Large Multidimensional Arrays | Shandian Zhe, Yuan Qi, Youngja Park, Zenglin Xu, Ian Molloy, Suresh Chari | To address this limitation, we propose Distributed infinite Tucker (DinTucker), a new hierarchical Bayesian model that enables local learning of InfTucker on subarrays and global information integration from local results. |
| 333 | Fast Nonsmooth Regularized Risk Minimization with Continuation | Shuai Zheng, Ruiliang Zhang, James T. Kwok | In this paper, we propose a continuation algorithm that is applicable to a large class of nonsmooth regularized risk minimization problems, can be flexibly used with a number of existing solvers for the underlying smoothed subproblem, and with convergence results on the whole algorithm rather than just one of its subproblems. |
| 334 | Transfer Learning for Cross-Language Text Categorization through Active Correspondences Construction | Joey Tianyi Zhou, Sinno Jialin Pan, Ivor W. Tsang, Shen-Shyang Ho | In this paper, we present a general framework to integrate active learning to construct correspondences between heterogeneous domains for HTL, namely HTL through active correspondences construction (HTLA). |
| 335 | Veto-Consensus Multiple Kernel Learning | Yuxun Zhou, Ninghang Hu, Costas J. Spanos | We propose Veto-Consensus Multiple Kernel Learning (VCMKL), a novel way of combining multiple kernels such that one class of samples is described by the logical intersection (consensus) of base kernelized decision rules, whereas the other classes by the union (veto) of their complements. |
| 336 | Deep Hashing Network for Efficient Similarity Retrieval | Han Zhu, Mingsheng Long, Jianmin Wang, Yue Cao | In this paper, we propose a novel Deep Hashing Network (DHN) architecture for supervised hashing, in which we jointly learn good image representation tailored to hash coding and formally control the quantization error. |
| 337 | Coupled Dictionary Learning for Unsupervised Feature Selection | Pengfei Zhu, Qinghua Hu, Changqing Zhang, Wangmeng Zuo | Instead, we proposed a novel coupled analysis-synthesis dictionary learning method, which is free of generating labels. |
| 338 | Stochastic Parallel Block Coordinate Descent for Large-Scale Saddle Point Problems | Zhanxing Zhu, Amos J. Storkey | We propose an efficient stochastic block coordinate descent method using adaptive primal-dual updates, which enables flexible parallel optimization for large-scale problems. |
| 339 | Temporal Vaccination Games under Resource Constraints | Abhijin Adiga, Anil Vullikanti | In this paper, we study temporal vaccination games for epidemics in the SI (susceptible-infectious) model, with resource constraints in the form of a repeated game in complex social networks, with budgets on the number of vaccines that can be taken at any time. |
| 340 | Detection of Plan Deviation in Multi-Agent Systems | Bikramjit Banerjee, Steven Loscalzo, Daniel Lucas Thompson | We establish its equivalence to the intractable version, and evaluate these techniques in some challenging tasks. |
| 341 | Complexity of Shift Bribery in Committee Elections | Robert Bredereck, Piotr Faliszewski, Rolf Niedermeier, Nimrod Talmon | We study the (parameterized) complexity of Shift Bribery for multiwinner voting rules. |
| 342 | Global Model Checking on Pushdown Multi-Agent Systems | Taolue Chen, Fu Song, Zhilin Wu | In this paper, we investigate model checking algorithms for variants of alternating-time temporal logics over PGSs, initiated by Murano and Perelli at IJCAI’15. |
| 343 | Frugal Bribery in Voting | Palash Dey, Neeldhara Misra, Y. Narahari | We introduce and study two important special cases of the bribery problem, namely, FRUGAL-BRIBERY and FRUGAL-$BRIBERY where the briber is frugal in nature. |
| 344 | Target Surveillance in Adversarial Environments Using POMDPs | Maxim Egorov, Mykel J. Kochenderfer, Jaak J. Uudmae | This paper introduces an extension of the target surveillance problem in which the surveillance agent is exposed to an adversarial ballistic threat. |
| 345 | Multi-Variable Agents Decomposition for DCOPs | Ferdinando Fioretto, William Yeoh, Enrico Pontelli | This paper proposes a novel Multi-Variable Agent (MVA) DCOP decompositiontechnique, which: (i) Exploits the co-locality of each agent’s variables, allowing us to adopt efficient centralized techniques within each agent; (ii) Enables the use of hierarchical parallel models and proposes the use of GPUs; and (iii) Reduces the amount of computation and communication required in several classes of DCOP algorithms. |
| 346 | Implicit Coordination in Crowded Multi-Agent Navigation | Julio Erasmo Godoy, Ioannis Karamouzas, Stephen J. Guy, Maria Gini | To address this problem, we propose a new distributed approach to coordinate the motions of agents in crowded environments. |
| 347 | Emergence of Social Punishment and Cooperation through Prior Commitments | The Anh Han | In this paper, we use evolutionary game theory to show that this antisocial behavior can be efficiently restrained by relying on prior commitments, wherein agents can arrange, prior to an interaction, agreements regarding posterior compensation by those who dishonor the agreements. |
| 348 | Efficient Computation of Emergent Equilibrium in Agent-Based Simulation | Zehong Hu, Meng Sha, Moath Jarrah, Jie Zhang, Hui Xi | In this paper, we propose a novel three-layer framework to efficiently compute emergent equilibriums. |
| 349 | Strengthening Agents Strategic Ability with Communication | Xiaowei Huang, Qingliang Chen, Kaile Su | In this paper, we argue that in many cases, a suitable amount of information is required to be communicated between agents to both enforce goals and keep privacy. |
| 350 | Model Checking Probabilistic Knowledge: A PSPACE Case | Xiaowei Huang, Marta Kwiatkowska | In this paper, we propose to work with an additional restriction that agent’s knowledge concerns a special class of atomic propositions. |
| 351 | Learning for Decentralized Control of Multiagent Systems in Large, Partially-Observable Stochastic Environments | Miao Liu, Christopher Amato, Emily P. Anesta, John Daniel Griffith, Jonathan P How | To accommodate more realistic scenarios, when such information is not available, this paper presents a policy-based reinforcement learning approach, which learns the agent policies based solely on trajectories generated by previous interaction with the environment (e.g., demonstrations). |
| 352 | Bayesian Learning of Other Agents’ Finite Controllers for Interactive POMDPs | Alessandro Panella, Piotr Gmytrasiewicz | Since this Bayesian inference task is not analytically tractable, we devise a Markov chain Monte Carlo algorithm to approximate the posterior distribution. |
| 353 | Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs | Philipp Robbel, Frans A. Oliehoek, Mykel J. Kochenderfer | We present an approach to mitigate this limitation for certain types of multiagent systems, exploiting a property that can be thought of as “anonymous influence” in the factored MDP. |
| 354 | ConTaCT: Deciding to Communicate during Time-Critical Collaborative Tasks in Unknown, Deterministic Domains | Vaibhav V. Unhelkar, Julie A. Shah | In this work, we develop an online, decentralized communication policy, ConTaCT, that enables agents to decide whether or not to communicate during time-critical collaborative tasks in unknown, deterministic environments. |
| 355 | Is It Harmful When Advisors Only Pretend to Be Honest? | Dongxia Wang, Tim Muller, Jie Zhang, Yang Liu | We propose random processes to model and measure dynamic attacks. |
| 356 | Robust Execution of BDI Agent Programs by Exploiting Synergies Between Intentions | Yuan Yao, Brian Logan, John Thangarajah | In this paper, we propose an alternative approach to recovering from execution failures that relies on exploiting positive interactions between an agent’s intentions. |
| 357 | Short Text Representation for Detecting Churn in Microblogs | Hadi Amiri, Hal Daume III | In this paper, we consider the problem of classifying micro-posts as churny or non-churny with respect to a given brand. |
| 358 | Topic Concentration in Query Focused Summarization Datasets | Tal Baumel, Raphael Cohen, Michael Elhadad | We introduce TD-QFS, a new QFS dataset with controlled levels of topic concentration. |
| 359 | Combining Retrieval, Statistics, and Inference to Answer Elementary Science Questions | Peter Clark, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Turney, Daniel Khashabi | In this paper, we describe an alternative approach that operates at three levels of representation and reasoning: information retrieval, corpus statistics, and simple inference over a semi-automatically constructed knowledge base, to achieve substantially improved results. |
| 360 | Verb Pattern: A Probabilistic Semantic Representation on Verbs | Wanyun Cui, Xiyou Zhou, Hangyu Lin, Yanghua Xiao, Haixun Wang, Seung-won Hwang, Wei Wang | In this paper, we introduce verb patterns to represent verbs’ semantics, such that each pattern corresponds to a single semantic of the verb. |
| 361 | ExTaSem! Extending, Taxonomizing and Semantifying Domain Terminologies | Luis Espinosa-Anke, Horacio Saggion, Francesco Ronzano, Roberto Navigli | We introduce ExTaSem! |
| 362 | A Unified Bayesian Model of Scripts, Frames and Language | Francis Ferraro, Benjamin Van Durme | We present the first probabilistic model to capture all levels of the Minsky Frame structure, with the goal of corpus-based induction of scenario definitions. |
| 363 | Single or Multiple? Combining Word Representations Independently Learned from Text and WordNet | Josu Goikoetxea, Eneko Agirre, Aitor Soroa | In this paper, we follow an alternative route. |
| 364 | Representing Verbs as Argument Concepts | Yu Gong, Kaiqi Zhao, Kenny Qili Zhu | We present a novel framework to automatically infer human readable and machine computable action concepts with high accuracy. |
| 365 | A Generative Model of Words and Relationships from Multiple Sources | Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch | We propose a generative model which integrates evidence from diverse data sources, enabling the sharing of semantic information. |
| 366 | Agreement on Target-Bidirectional LSTMs for Sequence-to-Sequence Learning | Lemao Liu, Andrew Finch, Masao Utiyama, Eiichiro Sumita | We propose a simple yet effective approach to overcome this shortcoming. |
| 367 | Fine-Grained Semantic Conceptualization of FrameNet | Jin-woo Park, Seung-won Hwang, Haixun Wang | Fine-Grained Semantic Conceptualization of FrameNet |
| 368 | Dependency Tree Representations of Predicate-Argument Structures | Likun Qiu, Yue Zhang, Meishan Zhang | We present a novel annotation framework for representing predicate-argument structures, which uses dependency trees to encode the syntactic and semantic roles of a sentence simultaneously. |
| 369 | Complementing Semantic Roles with Temporally Anchored Spatial Knowledge: Crowdsourced Annotations and Experiments | Alakananda Vempala, Eduardo Blanco | This paper presents a framework to infer spatial knowledge from semantic role representations. |
| 370 | Representation Learning of Knowledge Graphs with Entity Descriptions | Ruobing Xie, Zhiyuan Liu, Jia Jia, Huanbo Luan, Maosong Sun | In this paper, we propose a novel RL method for knowledge graphs taking advantages of entity descriptions. |
| 371 | Hashtag-Based Sub-Event Discovery Using Mutually Generative LDA in Twitter | Chen Xing, Yuan Wang, Jie Liu, Yalou Huang, Wei-Ying Ma | To this end, we propose a hashtag-based mutually generative Latent Dirichlet Allocation model(MGe-LDA). |
| 372 | PEAK: Pyramid Evaluation via Automated Knowledge Extraction | Qian Yang, Rebecca J. Passonneau, Gerard de Melo | We propose PEAK, the first method to automatically assess summary content using the pyramid method that also generates the pyramid content models. |
| 373 | Instructable Intelligent Personal Agent | Amos Azaria, Jayant Krishnamurthy, Tom M. Mitchell | We introduce our Learning by Instruction Agent (LIA), an intelligent personal agent that users can teach to perform new action sequences to achieve new commands, using solely natural language interaction. |
| 374 | Joint Word Representation Learning Using a Corpus and a Semantic Lexicon | Danushka Bollegala, Mohammed Alsuhaibani, Takanori Maehara, Ken-ichi Kawarabayashi | For this purpose, we propose a joint word representation learning method that simultaneously predictsthe co-occurrences of two words in a sentence subject to the relational constrains given by the semantic lexicon.We use relations that exist between words in the lexicon to regularize the word representations learnt from the corpus.Our proposed method statistically significantly outperforms previously proposed methods for incorporating semantic lexicons into wordrepresentations on several benchmark datasets for semantic similarity and word analogy. |
| 375 | Ask, and Shall You Receive? Understanding Desire Fulfillment in Natural Language Text | Snigdha Chaturvedi, Dan Goldwasser, Hal Daume III | We propose various unstructured and structured models that capture fulfillment cues such as the subject’s emotional state and actions. |
| 376 | Modeling Evolving Relationships Between Characters in Literary Novels | Snigdha Chaturvedi, Shashank Srivastava, Hal Daume III, Chris Dyer | We propose a semi-supervised framework to learn relationship sequences from fully as well as partially labeled data. |
| 377 | Jointly Modeling Topics and Intents with Global Order Structure | Bei Chen, Jun Zhu, Nan Yang, Tian Tian, Ming Zhou, Bo Zhang | The goal of this research is to capture the document intent structure by modeling documents as a mixture of topic words and rhetorical words. |
| 378 | Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text | Sahil Garg, Aram Galstyan, Ulf Hermjakob, Daniel Marcu | Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text |
| 379 | What Happens Next? Event Prediction Using a Compositional Neural Network Model | Mark Granroth-Wilding, Stephen Clark | We present a neural network model that simultaneously learns embeddings for words describing events, a function to compose the embeddings into a representation of the event, and a coherence function to predict the strength of association between two events. |
| 380 | A Representation Learning Framework for Multi-Source Transfer Parsing | Jiang Guo, Wanxiang Che, David Yarowsky, Haifeng Wang, Ting Liu | To address these two challenges, we present a novel representation learning framework for multi-source transfer parsing. |
| 381 | Character-Aware Neural Language Models | Yoon Kim, Yacine Jernite, David Sontag, Alexander M. Rush | We describe a simple neural language model that relies only on character-level inputs. |
| 382 | Implicit Discourse Relation Classification via Multi-Task Neural Networks | Yang Liu, Sujian Li, Xiaodong Zhang, Zhifang Sui | To exploit the combination of different discourse corpora, we design related discourse classification tasks specific to a corpus, and propose a novel Convolutional Neural Network embedded multi-task learning system to synthesize these tasks by learning both unique and shared representations for each task. |
| 383 | Convolution Kernels for Discriminative Learning from Streaming Text | Michal Lukasik, Trevor Cohn | We develop a method based on convolution kernels to model discriminative learning over streams of text. |
| 384 | Numerical Relation Extraction with Minimal Supervision | Aman Madaan, Ashish Mittal, . Mausam, Ganesh Ramakrishnan, Sunita Sarawagi | We design two extraction systems that require minimal human supervision per relation: (1) NumberRule, a rule based extractor, and (2) NumberTron, a probabilistic graphical model. |
| 385 | Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences | Hongyuan Mei, Mohit Bansal, Matthew R. Walter | We propose a neural sequence-to-sequence model for direction following, a task that is essential to realizing effective autonomous agents. |
| 386 | Addressing a Question Answering Challenge by Combining Statistical Methods with Inductive Rule Learning and Reasoning | Arindam Mitra, Chitta Baral | In this work, we present a system that excels at all the tasks except one. |
| 387 | Siamese Recurrent Architectures for Learning Sentence Similarity | Jonas Mueller, Aditya Thyagarajan | We present a siamese adaptation of the Long Short-Term Memory (LSTM) network for labeled data comprised of pairs of variable-length sequences. |
| 388 | Text Matching as Image Recognition | Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Shengxian Wan, Xueqi Cheng | Inspired by the success of convolutional neural network in image recognition, where neurons can capture many complicated patterns based on the extracted elementary visual patterns such as oriented edges and corners, we propose to model text matching as the problem of image recognition. |
| 389 | Learning Statistical Scripts with LSTM Recurrent Neural Networks | Karl Pichotta, Raymond J. Mooney | We describe a Recurrent Neural Network model for statistical script learning using Long Short-Term Memory, an architecture which has been demonstrated to work well on a range of Artificial Intelligence tasks. |
| 390 | Inferring Interpersonal Relations in Narrative Summaries | Shashank Srivastava, Snigdha Chaturvedi, Tom Mitchell | In this work, we address the problem of inferring the polarity of relationships between people in narrative summaries. |
| 391 | Evaluation of Semantic Dependency Labeling Across Domains | Svetlana Stoyanchev, Amanda Stent, Srinivas Bangalore | In this paper we: (a) outline a novel method for FrameNet-style semantic dependency labeling that builds on a syntactic dependency parse; and (b) compare the accuracy of domain-adapted and generic approaches to semantic parsing for dialog tasks, using a frame-annotated corpus of human-computer dialogs in an airline reservation domain. |
| 392 | Inside Out: Two Jointly Predictive Models for Word Representations and Phrase Representations | Fei Sun, Jiafeng Guo, Yanyan Lan, Jun Xu, Xueqi Cheng | Considering the advantages and limitations of both approaches, we propose two novel models to build better word representations by modeling both external contexts and internal morphemes in a jointly predictive way, called BEING and SEING. |
| 393 | Non-Linear Similarity Learning for Compositionality | Masashi Tsubaki, Kevin Duh, Masashi Shimbo, Yuji Matsumoto | Many NLP applications rely on the existence ofsimilarity measures over text data.Although word vector space modelsprovide good similarity measures between words,phrasal and sentential similarities derived from compositionof individual words remain as a difficult problem.In this paper, we propose a new method of ofnon-linear similarity learning for semantic compositionality.In this method, word representations are learnedthrough the similarity learning of sentencesin a high-dimensional space with kernel functions.On the task of predicting the semantic similarity oftwo sentences (SemEval 2014, Task 1),our method outperforms linear baselines,feature engineering approaches,recursive neural networks,and achieve competitive results with long short-term memory models. |
| 394 | A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations | Shengxian Wan, Yanyan Lan, Jiafeng Guo, Jun Xu, Liang Pang, Xueqi Cheng | To tackle this problem, we present a new deep architecture to match two sentences with multiple positional sentence representations. |
| 395 | Morphological Segmentation with Window LSTM Neural Networks | Linlin Wang, Zhu Cao, Yu Xia, Gerard de Melo | In this paper, we instead propose novel neural network architectures that learn the structure of input sequences directly from raw input words and are subsequently able to predict morphological boundaries. |
| 396 | Minimally-Constrained Multilingual Embeddings via Artificial Code-Switching | Michael Wick, Pallika Kanani, Adam Pocock | We present a method that consumes a large corpus of multilingual text and produces a single, unified word embedding in which the word vectors generalize across languages. |
| 397 | Syntactic Skeleton-Based Translation | Tong Xiao, Jingbo Zhu, Chunliang Zhang, Tongran Liu | In this paper we propose an approach to modeling syntactically-motivated skeletal structure of source sentence for machine translation. |
| 398 | A Morphology-Aware Network for Morphological Disambiguation | Eray Yildiz, Caglar Tirkaz, H. Bahadır Sahin, Mustafa Tolga Eren, Omer Ozan Sonmez | A morphological disambiguator is usedto select the correct morphological analysis of a word.Morphological disambiguation is important because itgenerally is one of the first steps of natural languageprocessing and its performance affects subsequent analyses.In this paper, we propose a system that uses deeplearning techniques for morphological disambiguation.Many of the state-of-the-art results in computer vision,speech recognition and natural language processinghave been obtained through deep learning models.However, applying deep learning techniques to morphologicallyrich languages is not well studied. |
| 399 | Building Earth Mover’s Distance on Bilingual Word Embeddings for Machine Translation | Meng Zhang, Yang Liu, Huanbo Luan, Maosong Sun, Tatsuya Izuha, Jie Hao | We introduce Earth Mover’s Distance to this task by providing a natural formulation that translates words in a holistic fashion, addressing the limitations of the nearest neighbor. |
| 400 | Semi-Supervised Multinomial Naive Bayes for Text Classification by Leveraging Word-Level Statistical Constraint | Li Zhao, Minlie Huang, Ziyu Yao, Rongwei Su, Yingying Jiang, Xiaoyan Zhu | In this paper, we propose a novel method to augment MNB-EM by leveraging the word-level statistical constraint to preserve the class distribution on words. |
| 401 | Labeling the Semantic Roles of Commas | Naveen Arivazhagan, Christos Christodoulopoulos, Dan Roth | This paper proposes a set of relations commas participate in, expanding on previous work in this area, and develops a new dataset annotated with this set of labels. |
| 402 | Collective Supervision of Topic Models for Predicting Surveys with Social Media | Adrian Benton, Michael J. Paul, Braden Hancock, Mark Dredze | We introduce and explore a variety of topic model variants and provide an empirical analysis, with conclusions of the most effective models for this task. |
| 403 | Distant IE by Bootstrapping Using Lists and Document Structure | Lidong Bing, Mingyang Ling, Richard C. Wang, William W. Cohen | We describe a way of reducing the noise associated with distant IE by identifying coupling constraints between potential instance labels. |
| 404 | TGSum: Build Tweet Guided Multi-Document Summarization Dataset | Ziqiang Cao, Chengyao Chen, Wenjie Li, Sujian Li, Furu Wei, Ming Zhou | This paper proposes an effective way to automatically collect large scales of news-related multi-document summaries with reference to social media’s reactions. We release this dataset for further research. |
| 405 | Joint Inference over a Lightly Supervised Information Extraction Pipeline: Towards Event Coreference Resolution for Resource-Scarce Languages | Chen Chen, Vincent Ng | To address these problems, we propose to perform joint inference over a lightly supervised IE pipeline, where all the models are trained using either active learning or unsupervised learning. |
| 406 | Discourse Relations Detection via a Mixed Generative-Discriminative Framework | Jifan Chen, Qi Zhang, Pengfei Liu, Xuanjing Huang | In this paper, we introduce a mixed generative-discriminative framework, in which we use vector offsets between embeddings of words to represent the semantic relations between text segments and Fisher kernel framework to convert a variable number of vector offsets into a fixed length vector. |
| 407 | Age of Exposure: A Model of Word Learning | Mihai Dascalu, Danielle S. McNamara, Scott Crossley, Stefan Trausan-Matu | In order to best capture how lexical associations are created between related concepts, we propose automated indices of word complexity based on Age of Exposure (AoE). |
| 408 | Acquiring Knowledge of Affective Events from Blogs Using Label Propagation | Haibo Ding, Ellen Riloff | Our goal is to automatically acquire knowledge of stereotypically positive and negative events from personal blogs. |
| 409 | To Swap or Not to Swap? Exploiting Dependency Word Pairs for Reordering in Statistical Machine Translation | Christian Hadiwinoto, Yang Liu, Hwee Tou Ng | In this paper, we present a novel reordering approach utilizing sparse features based on dependency word pairs. |
| 410 | Global Distant Supervision for Relation Extraction | Xianpei Han, Le Sun | In this paper, we propose a global distant supervision model for relation extraction, which can: 1) compensate the lack of supervision with a wide variety of indirect supervision knowledge; and 2) reduce the uncertainty in DS by performing joint inference across relation instances. |
| 411 | Extracting Topical Phrases from Clinical Documents | Yulan He | Experimental results on patients’ discharge summaries show that the proposed approach outperforms the state-of-the-art topical phrase extraction model on both perplexity and topic coherence measure and finds more interpretable topics. |
| 412 | Topical Analysis of Interactions Between News and Social Media | Ting Hua, Yue Ning, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan | The analysis of interactions between social media and traditional news streams is becoming increasingly relevant for a variety of applications, including: understanding the underlying factors that drive the evolution of data sources, tracking the triggers behind events, and discovering emerging trends.Researchers have explored such interactions by examining volume changes or information diffusions,however, most of them ignore the semantical and topical relationships between news and social media data.Our work is the first attempt to study how news influences social media, or inversely, based on topical knowledge.We propose a hierarchical Bayesian model that jointly models the news and social media topics and their interactions.We show that our proposed model can capture distinct topics for individual datasets as well as discover the topic influences among multiple datasets.By applying our model to large sets of news and tweets, we demonstrate its significant improvement over baseline methods and explore its power in the discovery of interesting patterns for real world cases. |
| 413 | News Verification by Exploiting Conflicting Social Viewpoints in Microblogs | Zhiwei Jin, Juan Cao, Yongdong Zhang, Jiebo Luo | In this paper, we take advantage of this “wisdom of crowds” information to improve news verification by mining conflicting viewpoints in microblogs. |
| 414 | Argument Mining from Speech: Detecting Claims in Political Debates | Marco Lippi, Paolo Torroni | The research question we address in this work is whether in such domains one can improve claim detection for argument mining, by employing features from text and speech in combination. |
| 415 | Improving Opinion Aspect Extraction Using Semantic Similarity and Aspect Associations | Qian Liu, Bing Liu, Yuanlin Zhang, Doo Soon Kim, Zhiqiang Gao | This paper proposes a novel unsupervised approach to make a major improvement. |
| 416 | A Probabilistic Soft Logic Based Approach to Exploiting Latent and Global Information in Event Classification | Shulin Liu, Kang Liu, Shizhu He, Jun Zhao | To resolve this problem, we propose a feasible approach which encodes global information in the form of logic using Probabilistic Soft Logic model. |
| 417 | Reading the Videos: Temporal Labeling for Crowdsourced Time-Sync Videos Based on Semantic Embedding | Guangyi Lv, Tong Xu, Enhong Chen, Qi Liu, Yi Zheng | In this paper, we propose a novel video understanding framework to assign temporal labels on highlighted video shots. |
| 418 | Joint Word Segmentation, POS-Tagging and Syntactic Chunking | Chen Lyu, Yue Zhang, Donghong Ji | Chinese chunking has traditionally been solved by assuming gold standard word segmentation.We find that the accuracies drop drastically when automatic segmentation is used.Inspired by the fact that chunking knowledge can potentially improve segmentation, we explore a joint model that performs segmentation, POS-tagging and chunking simultaneously.In addition, to address the sparsity of full chunk features, we employ a semi-supervised method to derive chunk cluster features from large-scale automatically-chunked data.Results show the effectiveness of the joint model with semi-supervised features. |
| 419 | Microsummarization of Online Reviews: An Experimental Study | Rebecca Mason, Benjamin Gaska, Benjamin Van Durme, Pallavi Choudhury, Ted Hart, Bill Dolan, Kristina Toutanova, Margaret Mitchell | In this paper, we introduce the task of microsummarization, which combines sentiment analysis, summarization, and entity recognition in order to surface key content to users. |
| 420 | A Semi-Supervised Learning Approach to Why-Question Answering | Jong-Hoon Oh, Kentaro Torisawa, Chikara Hashimoto, Ryu Iida, Masahiro Tanaka, Julien Kloetzer | We propose a semi-supervised learning method for improvingwhy-question answering (why-QA). |
| 421 | Discovering User Attribute Stylistic Differences via Paraphrasing | Daniel Preotiuc-Pietro, Wei Xu, Lyle Ungar | In this study, we aim to find linguistic style distinctions across three different user attributes: gender, age and occupational class. |
| 422 | Improving Twitter Sentiment Classification Using Topic-Enriched Multi-Prototype Word Embeddings | Yafeng Ren, Yue Zhang, Meishan Zhang, Donghong Ji | In particular, we develop two neural networks which 1) learn word embeddings that better capture tweet context by incorporating topic information, and 2) learn topic-enriched multiple prototype embeddings for each word.Experiments on Twitter sentiment benchmark datasets in SemEval 2013 show that TMWE outperforms the top system with hand-crafted features, and the current best neural network model. |
| 423 | Temporal Topic Analysis with Endogenous and Exogenous Processes | Baiyang Wang, Diego Klabjan | We propose a hierarchical Bayesian topic model which imposes a “group-correlated” hierarchical structure on the evolution of topics over time incorporating both processes, and show that this model can be estimated from Markov chain Monte Carlo sampling methods. |
| 424 | Identifying Search Keywords for Finding Relevant Social Media Posts | Shuai Wang, Zhiyuan Chen, Bing Liu, Sherry Emery | In this paper, we propose a novel technique to help the user identify topical search keywords. |
| 425 | Personalized Microblog Sentiment Classification via Multi-Task Learning | Fangzhao Wu, Yongfeng Huang | In this paper, we propose a personalized approach for microblog sentiment classification. |
| 426 | Improving Recommendation of Tail Tags for Questions in Community Question Answering | Yu Wu, Wei Wu, Zhoujun Li, Ming Zhou | To solve these problems, we propose matching questions and tags not only by themselves, but also by similar questions and similar tags. |
| 427 | Exploring Multiple Feature Spaces for Novel Entity Discovery | Zhaohui Wu, Yang Song, C. Lee Giles | We propose a principled approach that learns a novel entity classifier by modeling mention and entity representation into multiple feature spaces, including contextual, topical, lexical, neural embedding and query spaces. |
| 428 | Tweet Timeline Generation with Determinantal Point Processes | Jin-ge Yao, Feifan Fan, Wayne Xin Zhao, Xiaojun Wan, Edward Chang, Jianguo Xiao | This paper presents an approach based on determinantal point processes (DPPs) by jointly modeling the topical relevance of each selected tweet and overall selectional diversity. |
| 429 | Gated Neural Networks for Targeted Sentiment Analysis | Meishan Zhang, Yue Zhang, Duy-Tin Vo | In this paper, we extend this idea by proposing a sentence-level neural model to address the limitation of pooling functions, which do not explicitly model tweet-level semantics. |
| 430 | A Joint Model for Question Answering over Multiple Knowledge Bases | Yuanzhe Zhang, Shizhu He, Kang Liu, Jun Zhao | To this end, we present a novel joint model based on integer linear programming (ILP), uniting these two procedures into a uniform framework. |
| 431 | A Joint Model for Entity Set Expansion and Attribute Extraction from Web Search Queries | Zhenzhong Zhang, Le Sun, Xianpei Han | Based on this observation, we propose a joint model for ESE and AE, which models the inherent relationship between entities and attributes as a graph. |
| 432 | Aggregating Inter-Sentence Information to Enhance Relation Extraction | Hao Zheng, Zhoujun Li, Senzhang Wang, Zhao Yan, Jianshe Zhou | To effectively exploit inter-sentence information, we propose a ranking based approach, which first learns a scoring function based on a listwise learning-to-rank model and then uses it for multi-label relation extraction. |
| 433 | Dynamic Controllability of Disjunctive Temporal Networks: Validation and Synthesis of Executable Strategies | Alessandro Cimatti, Andrea Micheli, Marco Roveri | In this paper, we address the DC problem for a very general class of TNU, namely Disjunctive Temporal Network with Uncertainty. |
| 434 | Truncated Approximate Dynamic Programming with Task-Dependent Terminal Value | Amir-massoud Farahmand, Daniel N. Nikovski, Yuji Igarashi, Hiroki Konaka | We propose a new class of computationally fast algorithms to find close to optimal policy for Markov Decision Processes (MDP) with large finite horizon T.The main idea is that instead of planning until the time horizon T, we plan only up to a truncated horizon H << T and use an estimate of the true optimal value function as the terminal value. |
| 435 | General Error Bounds in Heuristic Search Algorithms for Stochastic Shortest Path Problems | Eric A. Hansen, Ibrahim Abdoulahi | We consider recently-derived error bounds that can be used to bound the quality of solutions found by heuristic search algorithms for stochastic shortest path problems. |
| 436 | Solving Risk-Sensitive POMDPs With and Without Cost Observations | Ping Hou, William Yeoh, Pradeep Varakantham | In this paper, unlike existing POMDP literature, we distinguish between the two cases of whether costs can or cannot be observed and show the empirical impact of cost observations. |
| 437 | Randomised Procedures for Initialising and Switching Actions in Policy Iteration | Shivaram Kalyanakrishnan, Neeldhara Misra, Aditya Gopalan | With the objective of furnishing improved upper bounds for PI, we introduce two randomised procedures in this paper. |
| 438 | Goal Recognition Design with Non-Observable Actions | Sarah Keren, Avigdor Gal, Erez Karpas | In this work we relax the full observability assumption of earlier work by offering a new generalized model for goal recognition design with non-observable actions. |
| 439 | Computing Contingent Plans Using Online Replanning | Radimir Komarnitsky, Guy Shani | In this paper we suggest a different approach – using an online contingent solver repeatedly to construct a plan tree. We present a set of experiments, showing our approach to scale better than state of the art offline planners. |
| 440 | Multi-Agent Path Finding with Payload Transfers and the Package-Exchange Robot-Routing Problem | Hang Ma, Craig Tovey, Guni Sharon, T. K. Satish Kumar, Sven Koenig | We study transportation problems where robots have to deliver packages and can transfer the packages among each other. |
| 441 | Solving Transition-Independent Multi-Agent MDPs with Sparse Interactions | Joris Scharpff, Diederik M. Roijers, Frans A. Oliehoek, Matthijs T. J. Spaan, Mathijs M. de Weerdt | We propose a new optimal solver for transition-independent MMDPs, in which agents can only affect their own state but their reward depends on joint transitions. |
| 442 | Solving Goal Recognition Design Using ASP | Tran Cao Son, Orkunt Sabuncu, Christian Schulz-Hanke, Torsten Schaub, William Yeoh | In this paper, we address the same problem with a different paradigm, namely, declarative approaches based on Answer Set Programming (ASP). |
| 443 | Efficient Macroscopic Urban Traffic Models for Reducing Congestion: A PDDL+ Planning Approach | Mauro Vallati, Daniele Magazzeni, Bart De Schutter, Lukas Chrpa, Thomas Leo McCluskey | Our goal is to show the feasibility of using mixed discrete-continuous planning to deal with unexpected circumstances in urban traffic control. |
| 444 | A Proactive Sampling Approach to Project Scheduling under Uncertainty | Pradeep Varakantham, Na Fu, Hoong Chuin Lau | We provide a principled approximation approach based on Sample Average Approximation (SAA) to compute proactive schedules for RCPSP/max with durational uncertainty. |
| 445 | A POMDP Formulation of Proactive Learning | Kyle Hollins Wray, Shlomo Zilberstein | We cast the Proactive Learning (PAL) problem—Active Learning (AL) with multiple reluctant, fallible, cost-varying oracles—as a Partially Observable Markov Decision Process (POMDP). |
| 446 | Approximation Algorithms for Route Planning with Nonlinear Objectives | Ger Yang, Evdokia Nikolova | We consider optimal route planning when the objective function is a general nonlinear and non-monotonic function. |
| 447 | Approximate Probabilistic Inference via Word-Level Counting | Supratik Chakraborty, Kuldeep S. Meel, Rakesh Mistry, Moshe Y. Vardi | In this work, we present the first approximate model counter that uses word-level hashing functions, and can directly leverage the power of sophisticated SMT solvers. |
| 448 | A Symbolic SAT-Based Algorithm for Almost-Sure Reachability with Small Strategies in POMDPs | Krishnendu Chatterjee, Martin Chmelík, Jessica Davies | We present a symbolic algorithm by an efficient encoding to SAT and using a SAT solver for the problem. |
| 449 | Structured Features in Naive Bayes Classification | Arthur Choi, Nazgol Tavabi, Adnan Darwiche | We propose the structured naive Bayes (SNB) classifier, which augments the ubiquitous naive Bayes classifier with structured features. |
| 450 | On Parameter Tying by Quantization | Li Chou, Somdeb Sarkhel, Nicholas Ruozzi, Vibhav Gogate | In this paper, we present an alternative variance reduction (regularization) technique that quantizes the MLE estimates as a post processing step, yielding a smoother model having several tied parameters. |
| 451 | Exact Sampling with Integer Linear Programs and Random Perturbations | Carolyn Kim, Ashish Sabharwal, Stefano Ermon | We propose a novel algorithm that views this as a combinatorial optimization problem and searches for the extreme state using a standard integer linear programming (ILP) solver, appropriately extended to account for the random perturbation. |
| 452 | From Exact to Anytime Solutions for Marginal MAP | Junkyu Lee, Radu Marinescu, Rina Dechter, Alexander Ihler | In this work, we explore the well known principle of weighted search for converting best-first search solvers into anytime schemes. |
| 453 | On Learning Causal Models from Relational Data | Sanghack Lee, Vasant Honavar | We describe RCD-Light, a sound and efficient constraint-based algorithm that is guaranteed to yield a correct partially-directed RCM structure with at least as many edges oriented as in that produced by RCD, the only other existing algorithm for learning RCM. |
| 454 | Online Spatio-Temporal Matching in Stochastic and Dynamic Domains | Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet | In this paper, we present a two stage stochastic optimization formulation to consider expected future demand. |
| 455 | Scaling Relational Inference Using Proofs and Refutations | Ravi Mangal, Xin Zhang, Aditya Kamath, Aditya V. Nori, Mayur Naik | We present an eager-lazy grounding algorithm that eagerly exploits proofs and lazily refutes counterexamples. |
| 456 | Closed-Form Gibbs Sampling for Graphical Models with Algebraic Constraints | Hadi Mohasel Afshar, Scott Sanner, Christfried Webers | Thus,our second contribution to address these challenges is to present a variation of Gibbs sampling that efficiently samples from these piecewise densities. |
| 457 | Learning Bayesian Networks with Bounded Tree-width via Guided Search | Siqi Nie, Cassio P. de Campos, Qiang Ji | We propose a guided search algorithm to find k-trees with maximum Informative scores, which is a measure of quality for the k-tree in yielding good Bayesian networks. |
| 458 | Learning Ensembles of Cutset Networks | Tahrima Rahman, Vibhav Gogate | In this paper, we take advantage of this unique property to develop fast algorithms for learning ensembles of cutset networks. |
| 459 | RAO*: An Algorithm for Chance-Constrained POMDP’s | Pedro Henrique Rodrigues Quemel e Assis Santana, Sylvie Thiébaux, Brian Williams | Our first contribution is a systematic derivation of execution risk in POMDP domains, which improves upon how chance constraints are handled in the constrained POMDP literature. |
| 460 | Separators and Adjustment Sets in Markov Equivalent DAGs | Benito van der Zander, Maciej Liskiewicz | In this paper we provide a new criterion which leads to an efficient algorithmic framework to find, test and enumerate covariate adjustments for chain graphs – mixed graphs representing in a compact way a broad range of Markov equivalence classes of DAGs. |
| 461 | Closing the Gap Between Short and Long XORs for Model Counting | Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon | We empirically demonstrate the effectiveness of these bounds on model counting benchmarks and in a Satisfiability Modulo Theory (SMT) application motivated by the analysis of contingency tables in statistics. |
| 462 | Distance Minimization for Reward Learning from Scored Trajectories | Benjamin Burchfiel, Carlo Tomasi, Ronald Parr | We examine a framework, Distance Minimization IRL (DM-IRL), for learning reward functions from scores an expert assigns to possibly suboptimal demonstrations. |
| 463 | Efficient Spatio-Temporal Tactile Object Recognition with Randomized Tiling Convolutional Networks in a Hierarchical Fusion Strategy | Lele Cao, Ramamohanarao Kotagiri, Fuchun Sun, Hongbo Li, Wenbing Huang, Zay Maung Maung Aye | The advancement of unsupervised feature learning and biological tactile sensing inspire us proposing the model of 3T-RTCN that performs spatio-temporal feature representation and fusion for tactile recognition. |
| 464 | Continual Planning in Golog | Till Hofmann, Tim Niemueller, Jens Claßen, Gerhard Lakemeyer | We address this challenge by integrating two seemingly different approaches — PDDL-based planning for efficient plan generation and Golog for highly expressive behavior specification — in a coherent framework that supports continual planning. |
| 465 | Selectively Reactive Coordination for a Team of Robot Soccer Champions | Juan Pablo Mendoza, Joydeep Biswas, Philip Cooksey, Richard Wang, Steven Klee, Danny Zhu, Manuela Veloso | This paper thus presents our Selectively Reactive Coordination (SRC) algorithm, consisting of two layers: A coordinated opponent-agnostic layer enables the team to create its own plans, setting the pace of the game in offense. |
| 466 | Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks | Peter Ondruska, Ingmar Posner | This paper presents to the best of our knowledge the first end-to-end object tracking approach which directly maps from raw sensor input to object tracks in sensor space without requiring any feature engineering or system identification in the form of plant or sensor models. |
| 467 | Component Caching in Hybrid Domains with Piecewise Polynomial Densities | Vaishak Belle, Guy Van den Broeck, Andrea Passerini | In this paper, as a first step in extending CC to hybrid domains, we show how propositional CC systems can be leveraged when limited to piecewise polynomial densities. |
| 468 | The Meta-Problem for Conservative Mal’tsev Constraints | Clement Carbonnel | We design an algorithm that decides in polynomial-time if a constraint language has a conservative Mal’tsev polymorphism, and outputs one if one exists. |
| 469 | Steiner Tree Problems with Side Constraints Using Constraint Programming | Diego de Uña, Graeme Gange, Peter Schachte, Peter J. Stuckey | We introduce here a propagator for the tree constraint with explanations, as well as lower bounding techniques and a novel constraint programming approach for the Steiner Tree Problem and two of its variants. |
| 470 | Alternative Filtering for the Weighted Circuit Constraint: Comparing Lower Bounds for the TSP and Solving TSPTW | Sylvain Ducomman, Hadrien Cambazard, Bernard Penz | We propose in this paper various filtering algorithms for the weighted circuit constraint which maintain a circuit in a weighted graph. |
| 471 | Using the Shapley Value to Analyze Algorithm Portfolios | Alexandre Fréchette, Lars Kotthoff, Tomasz Michalak, Talal Rahwan, Holger H. Hoos, Kevin Leyton-Brown | This paper argues for analyzing component algorithmcontributions via a measure drawn from coalitional game theory—the Shapleyvalue—and yields insight into a research community’s progress over time. |
| 472 | On the Extraction of One Maximal Information Subset That Does Not Conflict with Multiple Contexts | éric Grégoire, Yacine Izza, Jean-Marie Lagniez | In this paper, this question is addressed from a computational point of view in clausal Boolean logic. |
| 473 | Bidirectional Search That Is Guaranteed to Meet in the Middle | Robert C. Holte, Ariel Felner, Guni Sharon, Nathan R. Sturtevant | We present MM, the first bidirectional heuristic search algorithm whose forward and backward searches are guaranteed to ”meet in the middle”, i.e. never expand a node beyond the solution midpoint. |
| 474 | Breaking More Composition Symmetries Using Search Heuristics | Jimmy H. M. Lee, Zichen Zhu | In this paper, we give the first formal characterization of the pruning behavior of ParSBDS and its improved variants. |
| 475 | Increasing Nogoods in Restart-Based Search | Jimmy H. M. Lee, Christian Schulte, Zichen Zhu | We present a lighter weight filtering algorithm for incNGs in the context of restart-based search using dynamic event sets (dynamic subscriptions). |
| 476 | Exponential Recency Weighted Average Branching Heuristic for SAT Solvers | Jia Hui Liang, Vijay Ganesh, Pascal Poupart, Krzysztof Czarnecki | In this paper, we propose a new branching heuristic inspired by the exponential recency weighted average algorithm used to solve the bandit problem. |
| 477 | Counting-Based Search for Constraint Optimization Problems | Gilles Pesant | We propose an adaptation of counting-based search for optimization, show how to modify solution density computation for some of the most frequently-occurring constraints, and empirically evaluate its performance on several benchmark problems. |
| 478 | Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects | Hessam Bagherinezhad, Hannaneh Hajishirzi, Yejin Choi, Ali Farhadi | In this paper, we introduce a method to automatically infer object sizes, leveraging visual and textual information from web. We introduce the relative size dataset and show that our method outperforms competitive textual and visual baselines in reasoning about size comparisons. |
| 479 | Deep Quantization Network for Efficient Image Retrieval | Yue Cao, Mingsheng Long, Jianmin Wang, Han Zhu, Qingfu Wen | In this paper, we propose a novel Deep Quantization Network (DQN) architecture for supervised hashing, which learns image representation for hash coding and formally control the quantization error. |
| 480 | Dynamic Concept Composition for Zero-Example Event Detection | Xiaojun Chang, Yi Yang, Guodong Long, Chengqi Zhang, Alexander G. Hauptmann | In this paper, we focus on automatically detecting events in unconstrained videos without the use of any visual training exemplars. |
| 481 | Face Video Retrieval via Deep Learning of Binary Hash Representations | Zhen Dong, Su Jia, Tianfu Wu, Mingtao Pei | In this paper, we develop a new deep convolutional neural network (deep CNN) to learn discriminative and compact binary representations of faces for face video retrieval. |
| 482 | Zero-Shot Event Detection by Multimodal Distributional Semantic Embedding of Videos | Mohamed Elhoseiny, Jingen Liu, Hui Cheng, Harpreet Sawhney, Ahmed Elgammal | We propose a new zero-shot Event-Detection method by Multi-modal Distributional Semantic embedding of videos. |
| 483 | Concepts Not Alone: Exploring Pairwise Relationships for Zero-Shot Video Activity Recognition | Chuang Gan, Ming Lin, Yi Yang, Gerard de Melo, Alexander G. Hauptmann | To handle noise and non-linearities in the ranking scores of the selected concepts, we propose a novel pairwise order matrix approach for score aggregation. |
| 484 | Transductive Zero-Shot Recognition via Shared Model Space Learning | Yuchen Guo, Guiguang Ding, Xiaoming Jin, Jianmin Wang | An effective algorithm is proposed for optimization. |
| 485 | Reading Scene Text in Deep Convolutional Sequences | Pan He, Weilin Huang, Yu Qiao, Chen Change Loy, Xiaoou Tang | We develop a Deep-Text Recurrent Network (DTRN)that regards scene text reading as a sequence labelling problem. |
| 486 | Structured Output Prediction for Semantic Perception in Autonomous Vehicles | Rein Houthooft, Cedric De Boom, Stijn Verstichel, Femke Ongenae, Filip De Turck | The use of structured machine learning models is investigated, which not only allow for complex input, but also arbitrarily structured output. |
| 487 | Robust Complex Behaviour Modeling at 90Hz | Xiangyu Kong, Yizhou Wang, Tao Xiang | In this paper, we overcome these limitations by introducing a novel complex behaviour modeling framework, which consists of a Binarized Cumulative Directional (BCD) feature as representation, novel spatial and temporal context modeling via an iterative correlation maximization, and a set of behaviour models, each being a simple Bernoulli distribution. |
| 488 | Exploiting View-Specific Appearance Similarities Across Classes for Zero-Shot Pose Prediction: A Metric Learning Approach | Alina Kuznetsova, Sung Ju Hwang, Bodo Rosenhahn, Leonid Sigal | To address these problems, we propose a metric learning approach for joint class prediction and pose estimation. |
| 489 | Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision | Marius Leordeanu, Alexandra Radu, Shumeet Baluja, Rahul Sukthankar | We propose an efficient joint classifier learning and feature selection method that discovers sparse, compact representations of input features from a vast sea of candidates, with an almost unsupervised formulation. |
| 490 | Decentralized Robust Subspace Clustering | Bo Liu, Xiao-Tong Yuan, Yang Yu, Qingshan Liu, Dimitris N. Metaxas | We consider the problem of subspace clustering using the SSC (Sparse Subspace Clustering) approach, which has several desirable theoretical properties and has been shown to be effective in various computer vision applications.We develop a large scale distributed framework for the computation of SSC via an alternating direction method of multiplier (ADMM) algorithm. |
| 491 | Articulated Pose Estimation Using Hierarchical Exemplar-Based Models | Jiongxin Liu, Yinxiao Li, Peter Allen, Peter Belhumeur | Inspired by hierarchical object representation and recent application of Deep Convolutional Neural Networks (DCNNs) on human pose estimation, we propose a novel formulation that incorporates both hierarchical exemplar-based models and DCNNs in the spatial terms. |
| 492 | Multi-View 3D Human Tracking in Crowded Scenes | Xiaobai Liu | This paper presents a robust multi-view method for tracking people in 3D scene. |
| 493 | Face Model Compression by Distilling Knowledge from Neurons | Ping Luo, Zhenyao Zhu, Ziwei Liu, Xiaogang Wang, Xiaoou Tang | Unlike previousworks that represent the knowledge by the soften labelprobabilities, which are difficult to fit, we represent theknowledge by using the neurons at the higher hiddenlayer, which preserve as much information as the label probabilities, but are more compact. |
| 494 | Learning to Answer Questions from Image Using Convolutional Neural Network | Lin Ma, Zhengdong Lu, Hang Li | In this paper, we propose to employ the convolutional neural network (CNN) for the image question answering (QA) task. |
| 495 | SentiCap: Generating Image Descriptions with Sentiments | Alexander Patrick Mathews, Lexing Xie, Xuming He | We design a system to describe an image with emotions, and present a model that automatically generates captions with positive or negative sentiments. |
| 496 | Look, Listen and Learn — A Multimodal LSTM for Speaker Identification | Jimmy Ren, Yongtao Hu, Yu-Wing Tai, Chuan Wang, Li Xu, Wenxiu Sun, Qiong Yan | In this paper, we describe a novel multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies both visual and auditory modalities from the beginning of each sequence input. |
| 497 | Toward a Taxonomy and Computational Models of Abnormalities in Images | Babak Saleh, Ahmed Elgammal, Jacob Feldman, Ali Farhadi | In this paper we study various types of atypicalities in images in a more comprehensive way than has been done before. We propose a new dataset of abnormal images showing a wide range of atypicalities. |
| 498 | Domain-Constraint Transfer Coding for Imbalanced Unsupervised Domain Adaptation | Yao-Hung Hubert Tsai, Cheng-An Hou, Wei-Yu Chen, Yi-Ren Yeh, Yu-Chiang Frank Wang | In this paper, we particularly address the practical and challenging scenario of imbalanced cross-domain data. |
| 499 | Recognizing Actions in 3D Using Action-Snippets and Activated Simplices | Chunyu Wang, John Flynn, Yizhou Wang, Alan Yuille | We propose a novel representation for action-snippets, called activated simplices. |
| 500 | DARI: Distance Metric and Representation Integration for Person Verification | Guangrun Wang, Liang Lin, Shengyong Ding, Ya Li, Qing Wang | To explore their interaction, this work proposes an end-to-end learning framework called DARI, i.e. Distance metric And Representation Integration, and validates the effectiveness of DARI in the challenging task of person verification. |
| 501 | Video Semantic Clustering with Sparse and Incomplete Tags | Jingya Wang, Xiatian Zhu, Shaogang Gong | In this work, we develop a method for accuratelyclustering tagged videos based on a novel Hierarchical-MultiLabel Random Forest model capable of correlating structured visual and tag information. |
| 502 | Path Following with Adaptive Path Estimation for Graph Matching | Tao Wang, Haibin Ling | In this paper, we propose a novel path following strategy for graph matching aiming to improve its computation efficiency. |
| 503 | Pose-Guided Human Parsing by an AND/OR Graph Using Pose-Context Features | Fangting Xia, Jun Zhu, Peng Wang, Alan L. Yuille | In this paper, we propose a human parsing pipeline that uses pose cues, e.g., estimates of human joint locations, to provide pose-guided segment proposals for semantic parts. |
| 504 | Diversified Dynamical Gaussian Process Latent Variable Model for Video Repair | Hao Xiong, Tongliang Liu, Dacheng Tao | To address aforementioned problems, we propose the diversified dynamical Gaussian process latent variable model (D2GPLVM) for considering the variety in existing videos and thus introducing a diversity encouraging prior to inducing points. |
| 505 | Metric Embedded Discriminative Vocabulary Learning for High-Level Person Representation | Yang Yang, Zhen Lei, Shifeng Zhang, Hailin Shi, Stan Z. Li | In this paper, we propose a metric embedded discriminative vocabulary learning for high-level person representation with application to person re-identification. |
| 506 | Large Scale Similarity Learning Using Similar Pairs for Person Verification | Yang Yang, Shengcai Liao, Zhen Lei, Stan Z. Li | Unlike existing metric learning methods, we consider both the difference and commonness of an image pair to increase its discriminativeness. |
| 507 | Unsupervised Co-Activity Detection from Multiple Videos Using Absorbing Markov Chain | Donghun Yeo, Bohyung Han, Joon Hee Han | We propose a simple but effective unsupervised learning algorithm to detect a common activity (co-activity) from a set of videos, which is formulated using absorbing Markov chain in a principled way. |
| 508 | Discrete Image Hashing Using Large Weakly Annotated Photo Collections | Hanwang Zhang, Na Zhao, Xindi Shang, Huanbo Luan, Tat-seng Chua | We address the problem of image hashing by learning binary codes from large and weakly supervised photo collections. |
| 509 | Group Cost-Sensitive Boosting for Multi-Resolution Pedestrian Detection | Chao Zhu, Yuxin Peng | To address this problem, we propose in this paper a new multi-resolution detection approach based on a novel group cost-sensitive boosting algorithm, which extends the popular AdaBoost by exploring different costs for different resolution groups in the boosting process, and places more emphases on low resolution group in order to better handle detection of hard samples. |
| 510 | Learning Cross-Domain Neural Networks for Sketch-Based 3D Shape Retrieval | Fan Zhu, Jin Xie, Yi Fang | In this work, we address the sketch-based 3D shape retrieval problem with a novel Cross-Domain Neural Networks (CDNN) approach, which is further extended to Pyramid Cross-Domain Neural Networks (PCDNN) by cooperating with a hierarchical structure. |
| 511 | MC-HOG Correlation Tracking with Saliency Proposal | Guibo Zhu, Jinqiao Wang, Yi Wu, Xiaoyu Zhang, Hanqing Lu | In this paper, we propose a rich feature descriptor, MC-HOG, by leveraging rich gradient information across multiple color channels or spaces. |
| 512 | Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks | Wentao Zhu, Cuiling Lan, Junliang Xing, Wenjun Zeng, Yanghao Li, Li Shen, Xiaohui Xie | To train the deep LSTM network effectively, we propose a new dropout algorithm which simultaneously operates on the gates, cells, and output responses of the LSTM neurons. |
| 513 | Using Multiple Representations to Simultaneously Learn Computational Thinking and Middle School Science | Satabdi Basu, Gautam Biswas, John S. Kinnebrew | In this paper, we present an approach that leverages multiple linked representations to help students learn by constructing and analyzing computational models of science topics. |
| 514 | MIDCA: A Metacognitive, Integrated Dual-Cycle Architecture for Self-Regulated Autonomy | Michael T. Cox, Zohreh Alavi, Dustin Dannenhauer, Vahid Eyorokon, Hector Munoz-Avila, Don Perlis | We present a metacognitive, integrated, dual-cycle architecture whose function is to provide agents with a greater capacity for acting robustly in a dynamic environment and managing unexpected events. |
| 515 | Commonsense Interpretation of Triangle Behavior | Andrew S. Gordon | In this paper we model behavior interpretation as a process of logical abduction, where the reasoning task is to identify the most probable set of assumptions that logically entail the observable behavior of others, given commonsense theories of psychology and sociology. |
| 516 | Surprise-Triggered Reformulation of Design Goals | Kazjon Grace, Mary Lou Maher | This paper presents a cognitive model of goal formulation in designing that is triggered by surprise. |
| 517 | Visual Learning of Arithmetic Operation | Yedid Hoshen, Shmuel Peleg | A simple Neural Network model is presented for end-to-end visual learning of arithmetic operations from pictures of numbers. |
| 518 | Modeling Human Ad Hoc Coordination | Peter M. Krafft, Chris L. Baker, Alex "Sandy" Pentland, Joshua B. Tenenbaum | We introduce an exact algorithm for computing the infinitely recursive hierarchy of graded beliefs required for rational coordination in uncertain environments, and we introduce a novel mechanism for multiagent coordination that uses it. |
| 519 | Predicting Readers’ Sarcasm Understandability by Modeling Gaze Behavior | Abhijit Mishra, Diptesh Kanojia, Pushpak Bhattacharyya | We introduce a novel method to predict the sarcasm understandability of a reader. |
| 520 | Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model | Ryo Nakahashi, Chris L. Baker, Joshua B. Tenenbaum | These structures guide our ac- tion planning and execution, but when we observe others, the latent structure of their actions is typ- ically unobservable, and must be inferred in order to learn new skills by demonstration, or to as- sist others in completing their tasks. |
| 521 | Unsupervised Lexical Simplification for Non-Native Speakers | Gustavo H. Paetzold, Lucia Specia | We propose a novel, unsupervised approach for the task. |
| 522 | QA | Shourya Roy, Ragunathan Mariappan, Sandipan Dandapat, Saurabh Srivastava, Sainyam Galhotra, Balaji Peddamuthu | In this paper, we introduce an automatic real-time quality assurance system for contact centers — QART (pronounced cart). |
| 523 | Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models | Iulian V. Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, Joelle Pineau | We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. |
| 524 | Achieving Stable and Fair Profit Allocation with Minimum Subsidy in Collaborative Logistics | Lucas Agussurja, Hoong Chuin Lau, Shih-Fen Cheng | In this paper, we address this problem using the framework of computational cooperative games. |
| 525 | Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations | Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee | Specifically, we propose Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. |
| 526 | An Axiomatic Framework for Ex-Ante Dynamic Pricing Mechanisms in Smart Grid | Sambaran Bandyopadhyay, Ramasuri Narayanam, Pratyush Kumar, Sarvapali Ramchurn, Vijay Arya, Iskandarbin Petra | In particular, we propose an axiomatic framework that establishes the conceptual underpinnings of the class of ex-ante dynamic pricing schemes. |
| 527 | Multi-Instance Multi-Label Class Discovery: A Computational Approach for Assessing Bird Biodiversity | Forrest Briggs, Xiaoli Z. Fern, Raviv Raich, Matthew Betts | Specifically, given a large collectionof audio recordings containing bird and othersounds, we aim to automatically select a fixed size subsetof the recordings for human expert labeling suchthat the maximum number of species/classes is discovered.We employ a multi-instance multi-label representationto address multiple simultaneously vocalizingbirds with sounds that overlap in time, and proposenew algorithms for species/class discovery using thisrepresentation. |
| 528 | Multiagent-Based Route Guidance for Increasing the Chance of Arrival on Time | Zhiguang Cao, Hongliang Guo, Jie Zhang, Ulrich Fastenrath | Specifically, we propose a decentralized multiagent approach, where infrastructure agents locally collect intentions of concerned vehicle agents and formulate route guidance as a route assignment problem, to guarantee their arrival on time. |
| 529 | Understanding Dominant Factors for Precipitation over the Great Lakes Region | Soumyadeep Chatterjee, Stefan Liess, Arindam Banerjee, Vipin Kumar | In this work, we consider sparse regression, which simultaneously performs feature selection and regression, followed by random permutation tests for selecting dominant factors. |
| 530 | A Unifying Variational Inference Framework for Hierarchical Graph-Coupled HMM with an Application to Influenza Infection | Kai Fan, Chunyuan Li, Katherine Heller | The purpose of this paper is to build a unified learning framework for latent infection state estimation for the hGCHMM, regardless of the infection rate and transition function. |
| 531 | Topic Models to Infer Socio-Economic Maps | Lingzi Hong, Enrique Frias-Martinez, Vanessa Frias-Martinez | In this paper, we propose a novel approach whereby topic models are used to infer socio-economic levels from large-scale spatio-temporal data. |
| 532 | Energy- and Cost-Efficient Pumping Station Control | Timon V. Kanters, Frans A. Oliehoek, Michael Kaisers, Stan R. van den Bosch, Joep Grispen, Jeroen Hermans | In particular, we propose a light weight but realistic simulator and investigate if an online planning method (UCT) can utilise this simulator to improve the cost-efficiency of pumping station control policies. |
| 533 | Shortest Path Based Decision Making Using Probabilistic Inference | Akshat Kumar | We present a new perspective on the classical shortest path routing (SPR) problem in graphs. |
| 534 | Robust Decision Making for Stochastic Network Design | Akshat Kumar, Arambam James Singh, Pradeep Varakantham, Daniel Sheldon | We address the problem of robust decision making for stochastic network design. |
| 535 | Optimizing Infrastructure Enhancements for Evacuation Planning | Kunal Kumar, Julia Romanski, Pascal Van Hentenryck | The paper proposes a MIP model for deciding the most effective infrastructure upgrades as well as a Benders decomposition approach where the master problem jointly plans the upgrades and evacuation routes and the subproblem schedules the evacuation itself. |
| 536 | Spatially Regularized Streaming Sensor Selection | Changsheng Li, Fan Wei, Weishan Dong, Xiangfeng Wang, Junchi Yan, Xiaobin Zhu, Qingshan Liu, Xin Zhang | We propose to perform sensor selection in a multi-variate interpolation framework, such that the data sampled by the selected sensors can well predict those of the inactive sensors. |
| 537 | Preventing Illegal Logging: Simultaneous Optimization of Resource Teams and Tactics for Security | Sara Marie Mc Carthy, Milind Tambe, Christopher Kiekintveld, Meredith L. Gore, Alex Killion | This paper introduces a new, yet fundamental problem: SimultaneousOptimization of Resource Teams and Tactics (SORT). |
| 538 | Big-Data Mechanisms and Energy-Policy Design | Ankit Pat, Kate Larson, Srinivasen Keshav | In this paper we present an approach we call big-data mechanism design which combines a mechanism design framework with stakeholder surveys and data to allow policy-makers to gauge the costs and benefits of potential policy decisions.We illustrate the effectiveness of this approach in a concrete application domain: the peaksaver PLUS program in Ontario, Canada. |
| 539 | Benders Decomposition for Large-Scale Prescriptive Evacuations | Julia Romanski, Pascal Van Hentenryck | This paper considers prescriptive evacuation planning for a region threatened by a natural disaster such a flood, a wildfire, or a hurricane. |
| 540 | Predicting Spatio-Temporal Propagation of Seasonal Influenza Using Variational Gaussian Process Regression | Ransalu Senanayake, Simon O'Callaghan, Fabio Ramos | In this paper we develop a nonparametric model based on Gaussian process (GP) regression to capture the complex spatial and temporal dependencies present in the data. |
| 541 | Intelligent Habitat Restoration Under Uncertainty | Tommaso Urli, Jana Brotánková, Philip Kilby, Pascal Van Hentenryck | In this paper we present an intelligent system to assist conservation managers in planning habitat restoration actions, with focus on the activities to be carried out in the islands of the Great Barrier Reef (QLD) and the Pilbara (WA) regions of Australia. |
| 542 | Adaptable Regression Method for Ensemble Consensus Forecasting | John K. Williams, Peter P. Neilley, Joseph P. Koval, Jeff McDonald | This paper presents a method for combining multiple scalar forecasts to obtain deterministic predictions that are generally more accurate than any of the constituents. |
| 543 | Optimizing Resilience in Large Scale Networks | Xiaojian Wu, Daniel Sheldon, Shlomo Zilberstein | We propose a decision making framework to optimize the resilience of road networks to natural disasters such as floods. |
| 544 | Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping | Michael Xie, Neal Jean, Marshall Burke, David Lobell, Stefano Ermon | We therefore propose a transfer learning approach where nighttime light intensities are used as a data-rich proxy. |
| 545 | An Algorithm to Coordinate Measurements Using Stochastic Human Mobility Patterns in Large-Scale Participatory Sensing Settings | Alexandros Zenonos, Sebastian Stein, Nicholas R. Jennings | To address these shortcomings, we develop a computationally-efficient coordination algorithm (Best-match) to suggest to users where and when to take measurements. |
| 546 | Bagging Ensembles for the Diagnosis and Prognostication of Alzheimer’s Disease | Peng Dai, Femida Gwadry-Sridhar, Michael Bauer, Michael Borrie | Bagging Ensembles for the Diagnosis and Prognostication of Alzheimer’s Disease |
| 547 | Affective Personalization of a Social Robot Tutor for Children’s Second Language Skills | Goren Gordon, Samuel Spaulding, Jacqueline Kory Westlund, Jin Joo Lee, Luke Plummer, Marayna Martinez, Madhurima Das, Cynthia Breazeal | These signals were combined into a reward signal that fed into the robot’s affective reinforcement learning algorithm. |
| 548 | A Framework for Resolving Open-World Referential Expressions in Distributed Heterogeneous Knowledge Bases | Tom Williams, Matthias Scheutz | We present a domain-independent approach to reference resolution that allows a robotic or virtual agent to resolve references to entities (e.g., objects and locations) found in open worlds when the information needed to resolve such references is distributed among multiple heterogeneous knowledge bases in its architecture. |
| 549 | Deploying PAWS: Field Optimization of the Protection Assistant for Wildlife Security | Fei Fang, Thanh H. Nguyen, Rob Pickles, Wai Y. Lam, Gopalasamy R. Clements, Bo An, Amandeep Singh, Milind Tambe, Andrew Lemieux | To remedy this situation, prior work introduced a novel emerging application called PAWS (Protection Assistant for Wildlife Security); PAWS was proposed as a game-theoretic (“security games”) decision aid to optimize the use of patrolling resources. |
| 550 | Ontology Re-Engineering: A Case Study from the Automotive Industry | Nestor Rychtyckyj, Venkatesh Raman, Baskaran Sankaranarayanan, P. Sreenivasa Kumar, Deepak Khemani | In this paper, we will discuss the process by which we re-engineered the existing GSPAS KL-ONE ontology and deployed semantic web technology in our application. |
| 551 | Deploying nEmesis: Preventing Foodborne Illness by Data Mining Social Media | Adam Sadilek, Henry Kautz, Lauren DiPrete, Brian Labus, Eric Portman, Jack Teitel, Vincent Silenzio | We apply machine learning to Twitter data and develop a system that automatically detects venues likely to pose a public health hazard.Health professionals subsequently inspect individual flagged venues in a double blind experiment spanning the entire Las Vegas metropolitan area over three months. |
| 552 | An Autonomous Override System to Prevent Airborne Loss of Control | Sweewarman Balachandran, Ella Atkins | This paper presents a Flight Safety Assessment and Management (FSAM) decision system to reduce in-flight LOC risk. |
| 553 | Document Type Classification in Online Digital Libraries | Cornelia Caragea, Jian Wu, Sujatha Das Gollapalli, C. Lee Giles | We propose novel features that result in high-accuracy classifiers for document type classification. |
| 554 | Data-Augmented Software Diagnosis | Amir Elmishali, Roni Stern, Meir Kalech | Software fault prediction algorithms predict which software components is likely to contain faults using machine learning techniques. |
| 555 | Automated Regression Testing Using Constraint Programming | Arnaud Gotlieb, Mats Carlsson, Marius Liaeen, Dusica Marijan, Alexandre Petillon | In this paper, we address regression testing and TSR with Constraint Programming (CP). |
| 556 | Wikipedia in the Tourism Industry: Forecasting Demand and Modeling Usage Behavior | Pejman Khadivi, Naren Ramakrishnan | In this paper we explore the application of Wikipedia usage trends (WUTs) in tourism analysis. |
| 557 | Optimizing Energy Costs in a Zinc and Lead Mine | Alan Kinsella, Alan F. Smeaton, Barry Hurley, Barry O'Sullivan, Helmut Simonis | With these considerations in mind, this paper uses variable energy prices from Ireland’s Single Electricity Market, along with smart meter sensor data, to simulate the scheduling of an industrial-sized underground pump station in Tara Mines. |
| 558 | Automated Capture and Execution of Manufacturability Rules Using Inductive Logic Programming | Abha Moitra, Ravi Palla, Arvind Rangarajan | To facilitate this type of knowledge elicitation from Subject Matter Experts, we have developed a system that automatically generates formal and executable rules from provided labeled instance data. |
| 559 | MetaSeer.STEM:Towards Automating Meta-Analyses | Venkata Kishore Neppalli, Cornelia Caragea, Robin Mayes, Kim Nimon, Fred Oswald | In this paper, we propose a machine learning based system developed to support automated extraction of data pertinent to STEM education meta-analyses, including educational and human resource initiatives aimed at improving achievement, literacy and interest in the fields of science, technology, engineering, and mathematics. |
| 560 | Data Driven Game Theoretic Cyber Threat Mitigation | John Robertson, Vivin Paliath, Jana Shakarian, Amanda Thart, Paulo Shakarian | In this paper, we introduce a data-driven security game framework to model an attacker and provide policy recommendations to the defender. |
| 561 | Automated Volumetric Intravascular Plaque Classification Using Optical Coherence Tomography (OCT) | Ronny Shalev, Daisuke Nakamura, Setsu Nishino, Andrew Rollins, Hiram Bezerra, David Wilson, Soumya Ray | In this paper, we describe our approach, focusing on machine learning methods that are a key enabling technology. |
| 562 | A Hidden Markov Model Approach to Infer Timescales for High-Resolution Climate Archives | Mai Winstrup | We present a Hidden Markov Model-based algorithm for constructing timescales for paleoclimate records by annual layer counting. |
| 563 | Infusing Human Factors into Algorithmic Crowdsourcing | Han Yu, Chunyan Miao, Zhiqi Shen, Jun Lin, Cyril Leung, Qiang Yang | In this paper, we provide the research community with a new dataset derived from an online game-based platform to address this challenge. |
| 564 | Using Domain Knowledge to Improve Monte-Carlo Tree Search Performance in Parameterized Poker Squares | Robert Arrington, Clay Langley, Steven Bogaerts | This paper describes enhancements made to the MCTS algorithm to improve computer play, including pruning in the selection stage and a greedy simulation algorithm. |
| 565 | BeeMo, a Monte Carlo Simulation Agent for Playing Parameterized Poker Squares | Karo Castro-Wunsch, William Maga, Calin Anton | We investigated Parameterized Poker Squares to approximate an optimal game playing agent. |
| 566 | Conceptualizing Curse of Dimensionality with Parallel Coordinates | G. Devi, Charu Chauhan, Sutanu Chakraborti | We report on a novel use of parallel coordinates as a pedagogical tool for illustrating the non-intuitive properties of high dimensional spaces with special emphasis on the phenomenon of Curse of Dimensionality. |
| 567 | Teaching Big Data Analytics Skills with Intelligent Workflow Systems | Yolanda Gil | We have designed an open and modular course for data science and big data analytics using a workflow paradigm that allows students to easily experience big data through a sophisticated yet easy to use instrument that is an intelligent workflow system. |
| 568 | Design of an Online Course on Knowledge-Based AI | Ashok K. Goel, David A. Joyner | We describe the design, development and delivery of the online KBAI class in Fall 2014. |
| 569 | Learning and Using Hand Abstraction Values for Parameterized Poker Squares | Todd W. Neller, Colin M. Messinger, Zuozhi Yang | We describe the experimental development of an AI player that adapts to different point systems for Parameterized Poker Squares. |
| 570 | Creating Interactive and Visual Educational Resources for AI | Sameer Singh, Sebastian Riedel | In this paper, we introduce Moro, a software tool for easily creating and presenting AI-friendly teaching materials. |
| 571 | From the Lab to the Classroom and Beyond: Extending a Game-Based Research Platform for Teaching AI to Diverse Audiences | Nicole Sintov, Debarun Kar, Thanh Nguyen, Fei Fang, Kevin Hoffman, Arnaud Lyet, Milind Tambe | This paper describes our teaching approach for introducing the AI concepts underlying security games to diverse audiences. |
| 572 | The Turing Test in the Classroom | Lisa Torrey, Karen Johnson, Sid Sondergard, Pedro Ponce, Laura Desmond | This paper discusses the Turing Test as an educational activity for undergraduate students. |
| 573 | A Survey of Current Practice and Teaching of AI | Michael Wollowski, Robert Selkowitz, Laura E. Brown, Ashok Goel, George Luger, Jim Marshall, Andrew Neel, Todd Neller, Peter Norvig | In this paper, we present and briefly discuss the responses to those two surveys. |
| 574 | IRobot: Teaching the Basics of Artificial Intelligence in High Schools | Harald Burgsteiner, Martin Kandlhofer, Gerald Steinbauer | Therefore an innovative educational project teaching fundamental concepts of AI at high school level will be presented in this paper. |
| 575 | A.I. as an Introduction to Research Methods in Computer Science | Raghuram Ramanujan | In this paper, we propose a novel means for introducing undergraduate students to research experiences in computer science — via an introductory Artificial Intelligence (A.I.) course. |
| 576 | An Online Logic Programming Development Environment | Christian Reotutar, Mbathio Diagne, Evgenii Balai, Edward Wertz, Peter Lee, Shao-Lon Yeh, Yuanlin Zhang | We developed an online answer set programming environment with simple interface and self contained file system. |
| 577 | Using Declarative Programming in an Introductory Computer Science Course for High School Students | Maritza Reyes, Cynthia Perez, Rocky Upchurch, Timothy Yuen, Yuanlin Zhang | This paper describes the authors’ implementation of a declarative programming course for high school students during a 4-week summer session. |
| 578 | Teaching Automated Strategic Reasoning Using Capstone Tournaments | Oscar Veliz, Marcus Gutierrez, Christopher Kiekintveld | Courses in artificial intelligence and related topics often cover methods for reasoning under uncertainty, decision theory, and game theory. |
| 579 | Training Watson — A Cognitive Systems Course | Michael Wollowski | We developed a course in which students train an instance of Watson and develop an application that interacts with the trained instance. |
| 580 | Model AI Assignments 2016 | Todd W. Neller, Laura E. Brown, James B. Marshall, Lisa Torrey, Nate Derbinsky, Andrew A. Ward, Thomas E. Allen, Judy Goldsmith, Nahom Muluneh | Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2016 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. |
| 581 | Indefinite Scalability for Living Computation | David H. Ackley | In a question-and-answer format, this summary paper presents background material for the AAAI-16 Senior Member Presentation Track “Blue Sky Ideas” talk of the same name. |
| 582 | Embedding Ethical Principles in Collective Decision Support Systems | Joshua Greene, Francesca Rossi, John Tasioulas, Kristen Brent Venable, Brian Williams | Embedding Ethical Principles in Collective Decision Support Systems |
| 583 | Five Dimensions of Reasoning in the Wild | Don Perlis | Five Dimensions of Reasoning in the Wild |
| 584 | Ethical Dilemmas for Adaptive Persuasion Systems | Oliviero Stock, Marco Guerini, Fabio Pianesi | Still, ethical studies in this area are rare and tend to focus on the output of the required action; instead, this work focuses on the acceptability of persuasive acts themselves.Building systems able to persuade while being ethically acceptable requires that they be capable of intervening flexibly and of taking decisions about which specific persuasive strategy to use. |
| 585 | Ontology Instance Linking: Towards Interlinked Knowledge Graphs | Jeff Heflin, Dezhao Song | Ontology Instance Linking: Towards Interlinked Knowledge Graphs Finally, we present well-adopted evaluation datasets and metrics, and compare the performance of the state-of-the-art algorithms on such datasets. |
| 586 | Natural Language Processing for Enhancing Teaching and Learning | Diane Litman | This paper will organize and give an overview of research in this area, focusing on opportunities as well as challenges. |
| 587 | Strategic Behaviour When Allocating Indivisible Goods | Toby Walsh | We survey some recent research regarding strategic behaviour in resource allocation problems, focusing on the fair division of indivisible goods. |
| 588 | Rational Verification: From Model Checking to Equilibrium Checking | Michael Wooldridge, Julian Gutierrez, Paul Harrenstein, Enrico Marchioni, Giuseppe Perelli, Alexis Toumi | After motivating and introducing the framework of rational verification, we present formal models through which rational verification can be studied, and survey the complexity of key decision problems. |
| 589 | MIP-Nets: Enabling Information Sharing in Loosely-Coupled Teamwork | Ofra Amir, Barbara J. Grosz, Krzysztof Z. Gajos | In this paper, we formalize the problem of “information sharing in loosely-coupled extended-duration teamwork.” |
| 590 | Weighted A* Algorithms for Unsupervised Feature Selection with Provable Bounds on Suboptimality | Hiromasa Arai, Ke Xu, Crystal Maung, Haim Schweitzer | We propose an algorithm based on ideas similar to the Weighted A* algorithm in heuristic search. |
| 591 | Abstraction Using Analysis of Subgames | Anjon Basak | We compare this method with several variations of a common type of abstraction based on clustering similar strategies. |
| 592 | Bayesian Markov Games with Explicit Finite-Level Types | Muthukumaran Chandrasekaran, Yingke Chen, Prashant Doshi | We propose a new game-theoretic framework where Bayesian players engage in a Markov game and each has private but imperfect information regarding other players’ types. |
| 593 | BRBA: A Blocking-Based Association Rule Hiding Method | Peng Cheng, Ivan Lee, Li Li, Kuo-Kun Tseng, Jeng-Shyang Pan | This paper has proposed a blocking-based method to solve the association rule hiding problem for data sharing. |
| 594 | A CP-Based Approach for Popular Matching | Danuta Sorina Chisca, Mohamed Siala, Gilles Simonin, Barry O'Sullivan | We propose a constraint programming approach to the popular matching problem. |
| 595 | Predicting Prices in the Power TAC Wholesale Energy Market | Moinul Morshed Porag Chowdhury | I describe my work on using machine learning methods to predict prices in the Power TAC wholesale market, which will be used in future bidding strategies. |
| 596 | Robust Execution Strategies for Probabilistic Temporal Planning | Sam Dietrich, Kyle Lund, James C. Boerkoel | This paper introduces the Robust Execution Problem (REP) for finding maximally robust dispatch strategies for general probabilistic temporal planning problems. |
| 597 | A Comparison of Supervised Learning Algorithms for Telerobotic Control Using Electromyography Signals | Tyler M. Frasca, Antonio G. Sestito, Craig Versek, Douglas E. Dow, Barry C. Husowitz, Nate Derbinsky | In this preliminary work, we compare the accuracy and real-time performance of several machine-learning techniques for recognizing specific arm positions. |
| 598 | Trust and Distrust Across Coalitions: Shapley Value Based Centrality Measures for Signed Networks (Student Abstract Version) | Varun Gangal, Abhishek Narwekar, Balaraman Ravindran, Ramasuri Narayanam | We propose Shapley Value based centrality measures for signed social networks. |
| 599 | Authorship Attribution Using a Neural Network Language Model | Zhenhao Ge, Yufang Sun, Mark J. T. Smith | Here we investigate the performance of a feed-forward NNLM on an authorship attribution problem, with moderate author set size and relatively limited data. |
| 600 | Structure Aware L1 Graph for Data Clustering | Shuchu Han, Hong Qin | In this paper, we propose a Structure Aware (SA) L1 graph to improve the data clustering performance by capturing the manifold structure of input data. |
| 601 | Multivariate Conditional Outlier Detection and Its Clinical Application | Charmgil Hong, Milos Hauskrecht | This paper overviews and discusses our recent work on a multivariate conditional outlier detection framework for clinical applications. |
| 602 | Learning Complex Stand-Up Motion for Humanoid Robots | Heejin Jeong, Daniel D. Lee | In this abstract, we introduce complex stand-up motion of humanoid robots learned by using Reinforcement Learning. |
| 603 | Connecting the Dots Using Contextual Information Hidden in Text and Images | Md Abdul Kader, Sheikh Motahar Naim, Arnold P. Boedihardjo, M. Shahriar Hossain | This paper proposes a framework called Storyboarding that leverages unstructured text and images to explain events as sets of sub-events. |
| 604 | Monte Carlo Tree Search for Multi-Robot Task Allocation | Bilal Kartal, Ernesto Nunes, Julio Godoy, Maria Gini | We propose an efficient, satisficing and centralized Monte Carlo TreeSearch based algorithm exploiting branch and bound paradigm to solve the multi-robot task allocation problem with spatial, temporal and other side constraints. |
| 605 | Hierarchy Prediction in Online Communities | Denys Katerenchuk, Andrew Rosenberg | We propose to develop a natural language based ranking algorithm to predict user influence levels in online communication groups. |
| 606 | Handling Class Imbalance in Link Prediction Using Learning to Rank Techniques | Bopeng Li, Sougata Chaudhuri, Ambuj Tewari | We consider the link prediction (LP) problem in a partially observed network, where the objective is to make predictions in the unobserved portion of the network. |
| 607 | Predicting Links and Their Building Time: A Path-Based Approach | Manling Li, Yantao Jia, Yuanzhuo Wang, Zeya Zhao, Xueqi Cheng | In this paper, we propose a structure called the Time-Difference-Labeled Path, and a link prediction method (TDLP). |
| 608 | Social Emotion Classification via Reader Perspective Weighted Model | Xin Li, Yanghui Rao, Yanjia Chen, Xuebo Liu, Huan Huang | This paper is concerned with the classification of social emotions on varied-scale datasets. |
| 609 | Two-Stream Contextualized CNN for Fine-Grained Image Classification | Jiang Liu, Chenqiang Gao, Deyu Meng, Wangmeng Zuo | To alleviate this problem, in our work, we develop a novel approach, two-stream contextualized Convolutional Neural Network, which provides a simple but efficient context-content joint classification model under deep learning framework. |
| 610 | Decision Sum-Product-Max Networks | Mazen Melibari, Pascal Poupart, Prashant Doshi | In this paper, we propose a new extension to SPNs, called Decision Sum-Product-Max Networks (Decision-SPMNs), that makes SPNs suitable for discrete multi-stage decision problems. |
| 611 | Iterative Project Quasi-Newton Algorithm for Training RBM | Shuai Mi, Xiaozhao Zhao, Yuexian Hou, Peng Zhang, Wenjie Li, Dawei Song | In order to overcome this obstruction, we introduce an em-like iterative project quasi-Newton (IPQN) algorithm. |
| 612 | Pseudo-Tree Construction Heuristics for DCOPs with Variable Communication Times | Atena M Tabakhi | In this abstract, we incorporate non-uniform communication times in the default DCOP model and propose heuristics that exploit these communication times to speed up DCOP algorithms that operate on pseudo-trees. |
| 613 | A Word Embedding and a Josa Vector for Korean Unsupervised Semantic Role Induction | Kyeong-Min Nam, Yu-Seop Kim | We propose an unsupervised semantic role labeling method for Korean language, one of the agglutinative languages which have complicated suffix structures telling much of syntactic. |
| 614 | Conquering Adversary Behavioral Uncertainty in Security Games: An Efficient Modeling Robust Based Algorithm | Thanh Hong Nguyen, Arunesh Sinha, Milind Tambe | This paper therefore focuses on addressing behavioral uncertainty in SSG with the following main contributions: 1) we present a new uncertainty game model that integrates uncertainty intervals into a behavioral model to capture behavioral uncertainty; 2) based on this game model, we propose a novel robust algorithm that approximately computes the defender’s optimal strategy in the worst-case scenario of uncertainty—with a bound guarantee on its solution quality. |
| 615 | Bayesian AutoEncoder: Generation of Bayesian Networks with Hidden Nodes for Features | Kaneharu Nishino, Mary Inaba | We propose Bayesian AutoEncoder (BAE) in order to construct a recognition system which uses feedback information. |
| 616 | Human-Robot Trust and Cooperation Through a Game Theoretic Framework | Erin Paeng, Jane Wu, James C. Boerkoel | We propose using a coin entrustment game, a variant of prisoner’s dilemma, to measure trust and cooperation as separate phenomenon between human and robot agents. |
| 617 | Efficient Collaborative Crowdsourcing | Zhengxiang Pan, Han Yu, Chunyan Miao, Cyril Leung | We consider the problem of making efficient quality-time-cost trade-offs in collaborative crowdsourcing systems in which different skills from multiple workers need to be combined to complete a task. |
| 618 | SPAN: Understanding a Question with Its Support Answers | Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Xueqi Cheng | In this paper, we focus on the non-factoid questions and aim to pick out the best answer from its candidate answers. |
| 619 | Towards Structural Tractability in Hedonic Games | Dominik Peters | We investigate a structural way of achieving tractability, by requiring that agents’ preferences interact in a well-behaved manner. |
| 620 | Heuristic Planning for Hybrid Systems | Wiktor Mateusz Piotrowski, Maria Fox, Derek Long, Daniele Magazzeni, Fabio Mercorio | We developed DiNo, a new planner designed to tackle problems set in hybrid domains.DiNo is based on the discretise and validate approach and uses the novel Staged Relaxed Planning Graph+ (SRPG+) heuristic. |
| 621 | Counter-Transitivity in Argument Ranking Semantics | Fuan Pu, Jian Luo, Guiming Luo | In this work, we develop a formal theory about the argument ranking semantics based on this principle. |
| 622 | Discriminative Structure Learning of Arithmetic Circuits | Amirmohammad Rooshenas, Daniel Lowd | In this paper, we present the first discriminative structure learning algorithm for ACs, DACLearn (Discriminative AC Learner), which optimizes conditional log-likelihood. |
| 623 | Unsupervised Measure of Word Similarity: How to Outperform Co-Occurrence and Vector Cosine in VSMs | Enrico Santus, Alessandro Lenci, Tin-Shing Chiu, Qin Lu, Chu-Ren Huang | In this paper, we claim that vector cosine – which is generally considered among the most efficient unsupervised measures for identifying word similarity in Vector Space Models – can be outperformed by an unsupervised measure that calculates the extent of the intersection among the most mutually dependent contexts of the target words. |
| 624 | ROOT13: Spotting Hypernyms, Co-Hyponyms and Randoms | Enrico Santus, Alessandro Lenci, Tin-Shing Chiu, Qin Lu, Chu-Ren Huang | In this paper, we describe ROOT13, a supervised system for the classification of hypernyms, co-hyponyms and random words. |
| 625 | Abstracting Complex Domains Using Modular Object-Oriented Markov Decision Processes | Shawn Squire, Marie desJardins | We present an initial proposal for modular object-oriented MDPs, an extension of OO-MDPs that abstracts complex domains that are partially observable and stochastic with multiple goals. |
| 626 | Image Privacy Prediction Using Deep Features | Ashwini Kishore Tonge, Cornelia Caragea | In this study, we explore deep visual features and deep image tags for image privacy prediction. |
| 627 | Evaluating the Robustness of Game Theoretic Solutions When Using Abstraction | Oscar Samuel Veliz | We present an initial empirical study of the robustness of several solution methods when using abstracted games. |
| 628 | Text Simplification Using Neural Machine Translation | Tong Wang, Ping Chen, John Rochford, Jipeng Qiang | In this paper, we regard original English and simplified English as two languages, and apply a NMT model–Recurrent Neural Network (RNN) encoder-decoder on TS to make the neural network to learn text simplification rules by itself. |
| 629 | Business Event Curation: Merging Human and Automated Approaches | Yiqi Wang, Huiying Ma, Nichola Lowe, Maryann Feldman, Charles Schmitt | We present preliminary work to construct a knowledge curation system to advance research in the study of regional economics. |
| 630 | Direct Discriminative Bag Mapping for Multi-Instance Learning | Jia Wu, Shirui Pan, Peng Zhang, Xingquan Zhu | In this paper, we propose a direct discriminative mapping approach for multi-instance learning (MILDM), which identifies instances to directly distinguish bags in the new mapping space. |
| 631 | Mobility Sequence Extraction and Labeling Using Sparse Cell Phone Data | Yingxiang Yang, Peter Widhalm, Shounak Athavale, Marta C. Gonzalez | Mobility Sequence Extraction and Labeling Using Sparse Cell Phone Data |
| 632 | Epitomic Image Super-Resolution | Yingzhen Yang, Zhangyang Wang, Zhaowen Wang, Shiyu Chang, Ding Liu, Honghui Shi, Thomas S. Huang | We propose Epitomic Image Super-Resolution (ESR) to enhance the current internal SR methods that exploit the self-similarities in the input. |
| 633 | MicroScholar: Mining Scholarly Information from Chinese Microblogs | Yang Yu, Xiaojun Wan | In this paper, we briefly introduce the system framework and focus on the component of scholarly microblog categorization. |
| 634 | Intrinsic and Extrinsic Evaluations of Word Embeddings | Michael Zhai, Johnny Tan, Jinho D. Choi | Intrinsic and Extrinsic Evaluations of Word Embeddings |
| 635 | User-Centric Affective Computing of Image Emotion Perceptions | Sicheng Zhao, Hongxun Yao, Wenlong Xie, Xiaolei Jiang | We propose to predict the personalized emotion perceptions of images for each viewer. |
| 636 | Learning Structural Features of Nodes in Large-Scale Networks for Link Prediction | Aakas Zhiyuli, Xun Liang, Xiaoping Zhou | We present an algorithm (LsNet2Vec) that, given a large-scale network (millions of nodes), embeds the structural features of node into a lower and fixed dimensions of vector in the set of real numbers. |
| 637 | Interactive Learning and Analogical Chaining for Moral and Commonsense Reasoning | Joseph A. Blass | I propose to develop a model of repeated analogical chaining and analogical reasoning to enable autonomous agents to interactively learn to apply common sense and model an individual’s moral norms. |
| 638 | Machine Learning for Computational Psychology | Sarah M. Brown | I develop machine learning methods to overcome three initial technical barriers to application of the new paradigm. |
| 639 | Robust Learning from Demonstration Techniques and Tools | William Curran | My dissertation develops tools to allow persons with severe motor disabilities, and individuals in general, to train these systems. |
| 640 | Integrating Planning and Recognition to Close the Interaction Loop | Richard G. Freedman | In this thesis summary, I propose a framework that integrates these processes by taking advantage of features shared between them. |
| 641 | Apprenticeship Scheduling for Human-Robot Teams | Matthew C. Gombolay | The aim of my thesis is to develop an autonomous system that 1) learns the heuristics and implicit rules-of-thumb developed by domain experts from years of experience, 2) embeds and leverages this knowledge within a scalable resource optimization framework, and 3) provides decision support in a way that engages users and benefits them in their decision-making process. |
| 642 | Privacy Management in Agent-Based Social Networks | Nadin Kokciyan | This thesis proposes an agent-based representation of a social network, where the agents manage users’ privacy requirements and create privacy agreements with agents. |
| 643 | Multi-Modal Learning over User-Contributed Content from Cross-Domain Social Media | Wen-Yu Lee | The goal of the research is to discover and summarize data from the emerging social media into information of interests. |
| 644 | Unsupervised Learning of HTNs in Complex Adversarial Domains | Michael A. Leece | I plan to extend this work using the domain of real-time strategy (RTS) games, which have gained recent popularity as a challenging and complex domain for AI research. |
| 645 | Estimating Text Intelligibility via Information Packaging Analysis | Junyi Jessy Li | The goal of this thesis to analyze and address factors that influence the intelligibility of text from two aspects of information packaging: discourse structure and text specificity. |
| 646 | Robust Classification under Covariate Shift with Application to Active Learning | Anqi Liu | We propose to develop a general framework for classification under covariate shift that is robust, flexible and accurate. |
| 647 | Analogical Generalization of Linguistic Constructions | Clifton McFate | I propose an account that uses a computational model of analogy to learn and generalize argument structure constructions. |
| 648 | Writing Stories with Help from Recurrent Neural Networks | Melissa Roemmele | This thesis explores the use of a recurrent neural network model for a novel story generation task. |
| 649 | Scaling-Up MAP and Marginal MAP Inference in Markov Logic | Somdeb Sarkhel | The aim of the proposed thesis is to fill this void, by developing next generation inference algorithms for MAP and marginal MAP inference. |
| 650 | Adapting Plans through Communication with Unknown Teammates | Trevor Sarratt | The main contribution of this work is the characterization of the interaction between learning, communication, and planning in ad hoc teams. |
| 651 | Pragmatic Querying in Heterogeneous Knowledge Graphs | Amar Viswanathan | My thesis focuses on providing accessible Knowledge using Gricean notions of Cooperative Answering as a motivation. |
| 652 | Architectural Mechanisms for Situated Natural Language Understanding in Uncertain and Open Worlds | Tom Williams | My work towards this goal has primarily focused on two problems: (1) reference resolution, and (2) pragmatic reasoning. |
| 653 | Affective Computing and Applications of Image Emotion Perceptions | Sicheng Zhao, Hongxun Yao | The research of my PhD thesis focuses on image emotion computing (IEC), which aims to predict the emotion perceptions of given images. |
| 654 | What’s Hot in Human Language Technology: Highlights from NAACL HLT 2015 | Joyce Y. Chai, Anoop Sarkar, Rada Mihalcea | This paper shows a few examples to highlight the trends observed at the NAACL HLT 2015 conference. |
| 655 | What’s Hot in the Answer Set Programming Competition | Martin Gebser, Marco Maratea, Francesco Ricca | The ASP competition series aims at assessing and promoting the evolution of ASP systems and applications. |
| 656 | Inductive Logic Programming: Challenges | Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto | Inductive Logic Programming: Challenges |
| 657 | What’s Hot in Intelligent User Interfaces | Shimei Pan, Oliver Brdiczka, Giuseppe Carenini, Duen Horng Chau, Per Ola Kristensson | The ACM Conference on Intelligent User Interfaces (IUI) is the annual meeting of the intelligent user interface community and serves as a premier international forum for reporting outstanding research and development on intelligent user interfaces. |
| 658 | General Video Game AI: Competition, Challenges and Opportunities | Diego Perez-Liebana, Spyridon Samothrakis, Julian Togelius, Tom Schaul, Simon M. Lucas | The General Video Game AI framework and competition pose the problem of creating artificial intelligence that can play a wide, and in principle unlimited, range of games. |
| 659 | Angry Birds as a Challenge for Artificial Intelligence | Jochen Renz, XiaoYu Ge, Rohan Verma, Peng Zhang | In this paper we describe why this problem is a challenge for AI, and why it is an important step towards building AI that can successfully interact with the real world. |
| 660 | What’s Hot in Heuristic Search | Roni Stern, Levi H. S. Lelis | In this extended abstract we highlight recent efforts in understanding suboptimal search algorithms, as well as ensembles of heuristics and algorithms. |
| 661 | Competition of Distributed and Multiagent Planners (CoDMAP) | Michal Štolba, Antonín Komenda, Daniel L. Kovacs | In this paper we summarize course and highlights of the competition. |
| 662 | What’s Hot at RoboCup | Peter Stone | The aim of this paper is to give an overview of the latest and most innovative developments at RoboCup, as well as highlighting some of the current and future challenges upon which today’s RoboCup participants are focused. |
| 663 | Artificial Intelligence for Predictive and Evidence Based Architecture Design | Mehul Bhatt, Jakob Suchan, Carl Schultz, Vasiliki Kondyli, Saurabh Goyal | We employ a range of sensors for measuring the embodied visuo-locomotive experience of building users: eye-tracking, egocentric gaze analysis, external camera based visual analysis to interpret fine-grained behaviour (e.g., stopping, looking around, interacting with other people), and also manual observations made by human experimenters. |
| 664 | co-rank: An Online Tool for Collectively Deciding Efficient Rankings Among Peers | Ioannis Caragiannis, George A. Krimpas, Marianna Panteli, Alexandros A. Voudouris | Our aim with co-rank is to facilitate the grading of exams or assignments in massive open online courses (MOOCs). |
| 665 | SVVAMP: Simulator of Various Voting Algorithms in Manipulating Populations | François Durand, Fabien Mathieu, Ludovic Noirie | We present SVVAMP, a Python package dedicated to the study of voting systems with an emphasis on manipulation analysis. |
| 666 | Deploying PAWS to Combat Poaching: Game-Theoretic Patrolling in Areas with Complex Terrain (Demonstration) | Fei Fang, Thanh H. Nguyen, Rob Pickles, Wai Y. Lam, Gopalasamy R. Clements, Bo An, Amandeep Singh, Milind Tambe | Subsequent research has worked on the significant evolution of PAWS, from an emerging application to a regularly deployed software. |
| 667 | Predicting Gaming Related Properties from Twitter Accounts | Maria Ivanova Gorinova, Yoad Lewenberg, Yoram Bachrach, Alfredo Kalaitzis, Michael Fagan, Dean Carignan, Nitin Gautam | We present empirical results on the performance of our system based on its accuracy on our crowd-sourced dataset. |
| 668 | NLU Framework for Voice Enabling Non-Native Applications on Smart Devices | Soujanya Lanka, Deepika Pathania, Pooja Kushalappa, Pradeep Varakantham | To aid this demonstration, we have implemented the framework as a service in Android OS. |
| 669 | Modeling and Experimentation Framework for Fuzzy Cognitive Maps | Maikel Leon Espinosa, Gonzalo Napoles Ruiz | Design elements, and descriptions of the algorithms that have been incorporated into the software, and hybridized with Fuzzy Cognitive Maps, are presented in this paper. |
| 670 | Using Convolutional Neural Networks to Analyze Function Properties from Images | Yoad Lewenberg, Yoram Bachrach, Ian Kash, Peter Key | We propose algorithms for randomly constructing functions with convexity or symmetry properties, and use the images generated by these algorithms to train our network. |
| 671 | Predicting Personal Traits from Facial Images Using Convolutional Neural Networks Augmented with Facial Landmark Information | Yoad Lewenberg, Yoram Bachrach, Sukrit Shankar, Antonio Criminisi | To further improve performance, we propose a novel approach that incorporates facial landmark information for input images as an additional channel, helping the CNN learn face-specific features so that the landmarks across various training images hold correspondence. |
| 672 | EKNOT: Event Knowledge from News and Opinions in Twitter | Min Li, Jingjing Wang, Wenzhu Tong, Hongkun Yu, Xiuli Ma, Yucheng Chen, Haoyan Cai, Jiawei Han | We present the EKNOT system that automatically discovers major events from online news articles, connects each event to its discussion in Twitter, and provides a comprehensive summary of the events from both news media and social media’s point of view. |
| 673 | BBookX: Building Online Open Books for Personalized Learning | Chen Liang, Shuting Wang, Zhaohui Wu, Kyle Williams, Bart Pursel, Benjamin Brautigam, Sherwyn Saul, Hannah Williams, Kyle Bowen, C. Lee Giles | We demonstrate BBookX, a novel system that auto-matically builds in collaboration with a user online openbooks by searching open educational resources (OER). |
| 674 | Moodee: An Intelligent Mobile Companion for Sensing Your Stress from Your Social Media Postings | Huijie Lin, Jia Jia, Jie Huang, Enze Zhou, Jingtian Fu, Yejun Liu, Huanbo Luan | In this demo, we build a practical mobile application, Moodee,to help detect and release users’ psychological stress byleveraging users’ social media data in online social networks,and provide an interactive user interface to present users’and friends’ psychological stress states in an visualized andintuitional way.Given users’ online social media data as input, Moodee intelligentlyand automatically detects users’ stress states. |
| 675 | Write-righter: An Academic Writing Assistant System | Yuanchao Liu, Xin Wang, Ming Liu, Xiaolong Wang | This paper presents an academic writing assistant system called Write-righter, which can provide real-time hint and recommendation by analyzing the input context. |
| 676 | An Image Analysis Environment for Species Identification of Food Contaminating Beetles | Daniel Martin, Hongjian Ding, Leihong Wu, Howard Semey, Amy Barnes, Darryl Langley, Su Inn Park, Zhichao Liu, Weida Tong, Joshua Xu | Here we present an image analysis environment that allows practical deployment of the machine intelligence on computers with limited processing power and memory. |
| 677 | Jikan to Kukan: A Hands-On Musical Experience in AI, Games and Art | Georgia Rossmann Martins, Mário Escarce Junior, Leandro Soriano Marcolino | We offer a different perspective: we present the concept of “Art Games”, a view that opens up many possibilities for AI research and applications. |
| 678 | WWDS APIs: Application Programming Interfaces for Efficient Manipulation of World WordNet Database Structure | Hanumant Redkar, Sudha Bhingardive, Kevin Patel, Pushpak Bhattacharyya, Neha Prabhugaonkar, Apurva Nagvenkar, Ramdas Karmali | In this paper, we present WWDS APIs, which are designed to address this shortcoming. |
| 679 | Artificial Swarm Intelligence, a Human-in-the-Loop Approach to A.I. | Louis Rosenberg | This paper introduces UNU, an online platform that enables networked users to assemble in real-time swarms and tackle problems as an Artificial Swarm Intelligence (ASI). |
| 680 | Toward Interactive Relational Learning | Ryan Rossi, Rong Zhou | This paper introduces the Interactive Relational Machine Learning (iRML) paradigm in which users interactively design relational models by specifying the various components, constraints, and relational data representation, as well as perform evaluation, analyze errors, and make adjustments and refinements in a closed-loop. |
| 681 | EDDIE: An Embodied AI System for Research and Intervention for Individuals with ASD | Robert Selkowitz, Jonathan Rodgers, P. J. Moskal, Jon Mrowczynski, Christine Colson | We report on the ongoing development of EDDIE (Emotion Demonstration, Decoding, Interpretation, and Encoding), an interactive embodied AI to be deployed as an intervention system for children diagnosed with High-Functioning Autism Spectrum Disorders (HFASD). |
| 682 | A Tool to Graphically Edit CP-Nets | Aidan Shafran, Sam Saarinen, Judy Goldsmith | A Tool to Graphically Edit CP-Nets |
| 683 | A Visual Semantic Framework for Innovation Analytics | Walid Shalaby, Kripa Rajshekhar, Wlodek Zadrozny | In this demo we present a semantic framework for innovation and patent analytics powered by Mined Semantic Analysis (MSA). |
| 684 | Multi-Agent System Development MADE Easy | Zhiqi Shen, Han Yu, Chunyan Miao, Siyao Li, Yiqiang Chen | This paper outlines the MADE system, which provides an interactive platform for people who are not well-versed in AOSE to contribute to the rapid prototyping of MASs with ease. |
| 685 | A Fraud Resilient Medical Insurance Claim System | Yuliang Shi, Chenfei Sun, Qingzhong Li, Lizhen Cui, Han Yu, Chunyan Miao | This paper outlines HFDA, a hybrid AI approach to effectively and efficiently identify fraudulent medical insurance claims which has been tested in an online medical insurance claim system in China. |
| 686 | DECT: Distributed Evolving Context Tree for Understanding User Behavior Pattern Evolution | Xiaokui Shu, Nikolay Laptev, Danfeng (Daphne) Yao | Existing user behavior models abstracted as time-homogeneous Markov models cannot efficiently model user behavior variation through time. |
| 687 | Markov Argumentation Random Fields | Yuqing Tang, Nir Oren, Katia Sycara | We demonstrate an implementation of Markov Argumentation Random Fields (MARFs), a novel formalism combining elements of formal argumentation theory and probabilistic graphical models. |
| 688 | Shoot to Know What: An Application of Deep Networks on Mobile Devices | Jiaxiang Wu, Qinghao Hu, Cong Leng, Jian Cheng | In this demo, we propose the Quantized CNN (Q-CNN), an efficient framework for CNN models, to fulfill efficient and accurate image classification on mobile devices. |
| 689 | SAPE: A System for Situation-Aware Public Security Evaluation | Shu Wu, Qiang Liu, Ping Bai, Liang Wang, Tieniu Tan | In this work, we establish a Situation-Aware Public Security Evaluation (SAPE) platform. |
| 690 | Information Credibility Evaluation on Social Media | Shu Wu, Qiang Liu, Yong Liu, Liang Wang, Tieniu Tan | In this work, we establish a Network Information Credibility Evaluation (NICE) platform, which collects a database of rumors that have been verified on Sina Weibo and automatically evaluates the information generated by users on social media but has not been verified. |
| 691 | Productive Aging through Intelligent Personalized Crowdsourcing | Han Yu, Chunyan Miao, Siyuan Liu, Zhengxiang Pan, Nur Syahidah Bte Khalid, Zhiqi Shen, Cyril Leung | This paper outlines the Silver Productive (SP) mobile app, a system powered by the RTS-P intelligent personalized task sub-delegation approach with dynamic worker effort pricing functions. |