Paper Digest: KDD 2016 Highlights
To help the 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.
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Paper Digest Team
TABLE 1: KDD 2016 Papers
|Graphons and Machine Learning: Modeling and Estimation of Sparse Massive Networks
|How do we model and learn these networks?
|Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality
|Nando de Freitas
|In this talk I will show that learning-to-learn and compositionality are key ingredients for dealing with knowledge transfer so as to solve a wide range of tasks, for dealing with small-data regimes, and for continual learning.
|The Evolving Meaning of Information Security
|We will analyze the way in which these trends interact with others to create a situation in which what is possible in security and even the meaning of security in communication networks needs reexamination.
|People, Computers, and The Hot Mess of Real Data
|Joseph M. Hellerstein
|In this talk I’ll share some anecdotes from our research, user studies, and field experience with companies (Trifacta, Captricity), as well as an emerging open-source project (Ground).
|A VC View of Investing in ML
|This talk will give examples we are seeing (and funding!)
|Big Data Needs Big Dreamers: Lessons from Successful Big Data Investors
|Evangelos Simoudis, Mark Gorenberg, Tim Guleri, Matt Ocko, Greg Sands
|Big Data Needs Big Dreamers: Lessons from Successful Big Data Investors
|Designing Policy Recommendations to Reduce Home Abandonment in Mexico
|Klaus Ackermann, Eduardo Blancas Reyes, Sue He, Thomas Anderson Keller, Paul van der Boor, Romana Khan, Rayid Ghani, José Carlos González
|This paper describes our collaboration with Infonavit to reduce home abandonment at two levels: develop policy recommendations for targeted improvements in location characteristics, and develop a decision-support tool to assist the homeowner in the home location decision.
|Aircraft Trajectory Prediction Made Easy with Predictive Analytics
|Samet Ayhan, Hanan Samet
|In this paper, we describe a novel stochastic trajectory prediction approach for ATM that can be used for more efficient and realistic flight planning and to assist airspace flow management, potentially resulting in higher safety, capacity, and efficiency commensurate with fuel savings thereby reducing emissions for a better environment.
|Matrix Computations and Optimization in Apache Spark
|Reza Bosagh Zadeh, Xiangrui Meng, Alexander Ulanov, Burak Yavuz, Li Pu, Shivaram Venkataraman, Evan Sparks, Aaron Staple, Matei Zaharia
|The contributions described in this paper are already merged into Apache Spark and available on Spark installations by default, and commercially supported by a slew of companies which provide further services.
|Predicting Disk Replacement towards Reliable Data Centers
|Mirela Madalina Botezatu, Ioana Giurgiu, Jasmina Bogojeska, Dorothea Wiesmann
|In this paper, we present a highly accurate SMART-based analysis pipeline that can correctly predict the necessity of a disk replacement even 10-15 days in advance.
|Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights
|Joel Brooks, Matthew Kerr, John Guttag
|In this paper, we describe a novel player ranking system based entirely on the value of passes completed.
|The Legislative Influence Detector: Finding Text Reuse in State Legislation
|Matthew Burgess, Eugenia Giraudy, Julian Katz-Samuels, Joe Walsh, Derek Willis, Lauren Haynes, Rayid Ghani
|This paper presents the Legislative Influence Detector (LID).
|Identifying Police Officers at Risk of Adverse Events
|Samuel Carton, Jennifer Helsby, Kenneth Joseph, Ayesha Mahmud, Youngsoo Park, Joe Walsh, Crystal Cody, CPT Estella Patterson, Lauren Haynes, Rayid Ghani
|In this paper, we describe our work with the Charlotte-Mecklenburg Police Department (CMPD) to develop a machine learning model to predict which officers are at risk for an adverse event.
|Data-Driven Metric Development for Online Controlled Experiments: Seven Lessons Learned
|Alex Deng, Xiaolin Shi
|In this paper, we focus on the topic of how to develop meaningful and useful metrics for online services in their online experiments, and show how data-driven techniques and criteria can be applied in metric development process.
|Catch Me If You Can: Detecting Pickpocket Suspects from Large-Scale Transit Records
|Bowen Du, Chuanren Liu, Wenjun Zhou, Zhenshan Hou, Hui Xiong
|Existing studies on the AFC data have primarily focused on identifying passengers’ movement patterns.
|Email Volume Optimization at LinkedIn
|Rupesh Gupta, Guanfeng Liang, Hsiao-Ping Tseng, Ravi Kiran Holur Vijay, Xiaoyu Chen, Romer Rosales
|In this paper we discuss our strategy and experience with regard to the problem of email volume optimization at LinkedIn.
|Large-Scale Item Categorization in e-Commerce Using Multiple Recurrent Neural Networks
|Jung-Woo Ha, Hyuna Pyo, Jeonghee Kim
|Here, we demonstrate a successful report on a deep learning-based item categorization method, i.e., deep categorization network (DeepCN), in an e-commerce website.
|Online Dual Decomposition for Performance and Delivery-Based Distributed Ad Allocation
|Jim C. Huang, Rodolphe Jenatton, Cedric Archambeau
|In this paper, we present the online dual decomposition (ODD) framework for large-scale, online, distributed ad allocation, which combines dual decomposition and online convex optimization. We provide an extensive set of results from a large-scale production advertising system at Amazon to validate the framework and compare its behavior to various ad allocation algorithms.
|Minimizing Legal Exposure of High-Tech Companies through Collaborative Filtering Methods
|Bo Jin, Chao Che, Kuifei Yu, Yue Qu, Li Guo, Cuili Yao, Ruiyun Yu, Qiang Zhang
|Specifically, we propose two methods to meet the needs of both aims: a proximal slope one predictor and a time-aware predictor.
|Ranking Universities Based on Career Outcomes of Graduates
|Navneet Kapur, Nikita Lytkin, Bee-Chung Chen, Deepak Agarwal, Igor Perisic
|In this paper, we addresses these challenges holistically by developing a novel methodology for ranking and recommending universities for different professions on the basis of career outcomes of professionals who graduated from those schools.
|Predictors without Borders: Behavioral Modeling of Product Adoption in Three Developing Countries
|Muhammad R. Khan, Joshua E. Blumenstock
|In this paper, we develop a predictive model of Mobile Money adoption that uses billions of mobile phone communications records to understand the behavioral determinants of adoption.
|Repeat Buyer Prediction for E-Commerce
|Guimei Liu, Tam T. Nguyen, Gang Zhao, Wei Zha, Jianbo Yang, Jianneng Cao, Min Wu, Peilin Zhao, Wei Chen
|In this paper, we present our winning solution, which consists of comprehensive feature engineering and model training.
|Audience Expansion for Online Social Network Advertising
|Haishan Liu, David Pardoe, Kun Liu, Manoj Thakur, Frank Cao, Chongzhe Li
|In this paper, we describe the details of these methods, present in-depth analysis of their trade-offs, and demonstrate a hybrid strategy that possesses the combined strength of both methods.
|From Online Behaviors to Offline Retailing
|Ping Luo, Su Yan, Zhiqiang Liu, Zhiyong Shen, Shengwen Yang, Qing He
|In this study, we formulate this task as a cross-modality recommendation problem, and present its solution via a proposed probabilistic graphical model, called Online-to-Offline Topic Modeling (O2OTM).
|Firebird: Predicting Fire Risk and Prioritizing Fire Inspections in Atlanta
|Michael Madaio, Shang-Tse Chen, Oliver L. Haimson, Wenwen Zhang, Xiang Cheng, Matthew Hinds-Aldrich, Duen Horng Chau, Bistra Dilkina
|In collaboration with AFRD, we developed the Firebird framework to help municipal fire departments identify and prioritize commercial property fire inspections, using machine learning, geocoding, and information visualization.
|DopeLearning: A Computational Approach to Rap Lyrics Generation
|Eric Malmi, Pyry Takala, Hannu Toivonen, Tapani Raiko, Aristides Gionis
|We present a rap lyrics generation method that captures both of these aspects.
|EMBERS at 4 years: Experiences operating an Open Source Indicators Forecasting System
|Sathappan Muthiah, Patrick Butler, Rupinder Paul Khandpur, Parang Saraf, Nathan Self, Alla Rozovskaya, Liang Zhao, Jose Cadena, Chang-Tien Lu, Anil Vullikanti, Achla Marathe, Kristen Summers, Graham Katz, Andy Doyle, Jaime Arredondo, Dipak K. Gupta, David Mares, Naren Ramakrishnan
|In this paper, we describe our experiences operating EMBERS continuously for nearly 4 years, with specific attention to the discoveries it has enabled, correct as well as missed forecasts, lessons learnt from participating in a forecasting tournament, and our perspectives on the limits of forecasting including ethical considerations.
|Anomaly Detection Using Program Control Flow Graph Mining From Execution Logs
|Animesh Nandi, Atri Mandal, Shubham Atreja, Gargi B. Dasgupta, Subhrajit Bhattacharya
|We focus on the problem of detecting anomalous run-time behavior of distributed applications from their execution logs.
|Engagement Capacity and Engaging Team Formation for Reach Maximization of Online Social Media Platforms
|Alexander Nikolaev, Shounak Gore, Venu Govindaraju
|We show how engagement capacity can be useful in characterizing forum user behavior and in the reach maximization efforts.
|Boosted Decision Tree Regression Adjustment for Variance Reduction in Online Controlled Experiments
|Alexey Poyarkov, Alexey Drutsa, Andrey Khalyavin, Gleb Gusev, Pavel Serdyukov
|We focus on the problem of variance reduction for engagement metrics of user loyalty that are widely used in A/B testing of web services.
|Dynamic and Robust Wildfire Risk Prediction System: An Unsupervised Approach
|Mahsa Salehi, Laura Irina Rusu, Timothy Lynar, Anna Phan
|In this paper we propose a data-driven approach to predict wildfire risk using weather data.
|Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features
|Ying Shan, T. Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, JC Mao
|This paper proposes the Deep Crossing model which is a deep neural network that automatically combines features to produce superior models.
|Question Independent Grading using Machine Learning: The Case of Computer Program Grading
|Gursimran Singh, Shashank Srikant, Varun Aggarwal
|In this work, we propose a method to transform those features into a set of features that maintain their structural relation with the labels across questions.
|Contextual Intent Tracking for Personal Assistants
|Yu Sun, Nicholas Jing Yuan, Yingzi Wang, Xing Xie, Kieran McDonald, Rui Zhang
|To solve the intent tracking problem, we propose the Kalman filter regularized PARAFAC2 (KP2) nowcasting model, which compactly represents the structure and co-movement of context and intent.
|An Empirical Study on Recommendation with Multiple Types of Feedback
|Liang Tang, Bo Long, Bee-Chung Chen, Deepak Agarwal
|This paper presents an empirical study on various training methods for incorporating multiple user feedback types based on LinkedIn recommendation products.
|An Engagement-Based Customer Lifetime Value System for E-commerce
|Ali Vanderveld, Addhyan Pandey, Angela Han, Rajesh Parekh
|Here we describe a new CLTV system that solves these problems.
|Identifying Earmarks in Congressional Bills
|Ellery Wulczyn, Madian Khabsa, Vrushank Vora, Matthew Heston, Joe Walsh, Christopher Berry, Rayid Ghani
|In this paper, we present a machine learning system for automatically extracting earmarks from congressional bills and reports. Using this system, we construct the first publicly available database of earmarks dating back to 1995.
|Evaluating Mobile Apps with A/B and Quasi A/B Tests
|Ya Xu, Nanyu Chen
|We propose and establish quasi-experimental techniques for measuring impact from mobile app release, with results shared from a recent major app launch at LinkedIn.
|Ranking Relevance in Yahoo Search
|Dawei Yin, Yuening Hu, Jiliang Tang, Tim Daly, Mianwei Zhou, Hua Ouyang, Jianhui Chen, Changsung Kang, Hongbo Deng, Chikashi Nobata, Jean-Marc Langlois, Yi Chang
|In this paper, we give an overview of the solutions for relevance in the Yahoo search engine.
|Identifying Decision Makers from Professional Social Networks
|Shipeng Yu, Evangelia Christakopoulou, Abhishek Gupta
|In this paper we present LDMS, the LinkedIn Decision Maker Score, to quantify the ability of making a sales decision for each of the 400M+ LinkedIn members.
|Batch Model for Batched Timestamps Data Analysis with Application to the SSA Disability Program
|Qingqi Yue, Ao Yuan, Xuan Che, Minh Huynh, Chunxiao Zhou
|As the CPMS timestamps data of case status codes showed apparent batch patterns, we proposed a batch model and applied the constrained least squares method to estimate the mean service times and the variances.
|Collaborative Knowledge Base Embedding for Recommender Systems
|Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, Wei-Ying Ma
|In this paper, we investigate how to leverage the heterogeneous information in a knowledge base to improve the quality of recommender systems.
|GLMix: Generalized Linear Mixed Models For Large-Scale Response Prediction
|XianXing Zhang, Yitong Zhou, Yiming Ma, Bee-Chung Chen, Liang Zhang, Deepak Agarwal
|In this paper, we report how we successfully overcame the scalability bottleneck by applying parallelized block coordinate descent under the Bulk Synchronous Parallel (BSP) paradigm.
|A Non-parametric Approach to Detect Epileptogenic Lesions using Restricted Boltzmann Machines
|Yijun Zhao, Bilal Ahmed, Thomas Thesen, Karen E. Blackmon, Jennifer G. Dy, Carla E. Brodley, Ruben Kuzniekcy, Orrin Devinsky
|In this paper, we introduce a new approach which combines the restricted Boltzmann machines and a Bayesian non-parametric mixture model to address these issues.
|Recruitment Market Trend Analysis with Sequential Latent Variable Models
|Chen Zhu, Hengshu Zhu, Hui Xiong, Pengliang Ding, Fang Xie
|To this end, in this paper, we propose a new research paradigm for recruitment market analysis by leveraging unsupervised learning techniques for automatically discovering recruitment market trends based on large-scale recruitment data.
|Days on Market: Measuring Liquidity in Real Estate Markets
|Hengshu Zhu, Hui Xiong, Fangshuang Tang, Qi Liu, Yong Ge, Enhong Chen, Yanjie Fu
|To this end, in this paper, we aim to measure real estate liquidity by examining multiple factors in a holistic manner.
|Can You Teach the Elephant to Dance? AKA: Culture Eats Data Science for Breakfast
|This talk will review some practical realities of instituting data-driven decisions in a very large multi-national company.
|How Machine Learning has Finally Solved Wanamaker’s Dilemma
|Well, I’ll show you that some of the best targeted consumer marketing still suffers exactly from this problem, and tell you why — it’s not humanly possible to solve it!
|Learning Sparse Models at Scale
|In this talk, I will share lessons learned over the past 10 years when learning predictive models based on sparse data: 1) how to scale the inference algorithms to distributed data setting, 2) how to automate the learning process by reducing the amount of hyper-parameters to zero, 3) how to deal with Zipf distributions when learning resource-constrained models, and 4) how to combine dense and sparse-learning algorithms.
|Profiling Users from Online Social Behaviors with Applications for Tencent Social Ads
|In this talk, we will share our experience in large scale user data mining based on online social activities.
|The Wisdom of Crowds: Best Practices for Data Prep & Machine Learning Derived from Millions of Data Science Workflows
|In this presentation, I will take you on a tour of machine learning which spans the last 15 years of research and industry applications and share key insights with you about how data scientists perform their daily analysis tasks.
|Bayesian Optimization and Embedded Learning Systems
|I will discuss Bayesian optimization methods and their application in robotics and scientific applications, focusing on scaling up the dimensionality and managing multi-fidelity evaluations.
|Accelerating the Race to Autonomous Cars
|Accelerating the Race to Autonomous Cars
|Large-Scale Machine Learning at Verizon: Theory and Applications
|This talk will cover recent innovations in large-scale machine learning and their applications on massive, real-world data sets at Verizon.
|Computational Social Science: Exciting Progress and Future Challenges
|In this talk I highlight some examples of research that would not have been possible just a handful of years ago and that illustrate the promise of CSS.
|MAP: Frequency-Based Maximization of Airline Profits based on an Ensemble Forecasting Approach
|Bo An, Haipeng Chen, Noseong Park, V.S. Subrahmanian
|Compared with past methods to forecast market share and demand along airline routes, we introduce a novel Ensemble Forecasting (MAP-EF) approach considering two new classes of features: (i) features derived from clusters of similar routes, and (ii) features based on equilibrium pricing.
|Gemello: Creating a Detailed Energy Breakdown from Just the Monthly Electricity Bill
|Nipun Batra, Amarjeet Singh, Kamin Whitehouse
|In this paper, we propose a more scalable solution called Gemello that estimates the energy breakdown for one home by matching it with similar homes for which the breakdown is already known.
|CaSMoS: A Framework for Learning Candidate Selection Models over Structured Queries and Documents
|Fedor Borisyuk, Krishnaram Kenthapadi, David Stein, Bo Zhao
|We propose CaSMoS, a machine learned candidate selection framework that makes use of Weighted AND (WAND) query.
|Domain Adaptation in the Absence of Source Domain Data
|Boris Chidlovskii, Stephane Clinchant, Gabriela Csurka
|In this paper we address the domain adaptation problem in real world applications, where the reuse of source domain data is limited to classification rules or a few representative examples.
|Kam1n0: MapReduce-based Assembly Clone Search for Reverse Engineering
|Steven H.H. Ding, Benjamin C.M. Fung, Philippe Charland
|We propose a new variant of LSH scheme and incorporate it with graph matching to address these challenges.
|Joint Optimization of Multiple Performance Metrics in Online Video Advertising
|Sahin Cem Geyik, Sergey Faleev, Jianqiang Shen, Sean O’Donnell, Santanu Kolay
|In this paper, we explore the newly popularized space of online video advertising, where brand recognition is the key focus.
|Convolutional Neural Networks for Steady Flow Approximation
|Xiaoxiao Guo, Wei Li, Francesco Iorio
|We propose a general and flexible approximation model for real-time prediction of non-uniform steady laminar flow in a 2D or 3D domain based on convolutional neural networks (CNNs).
|Computational Drug Repositioning Using Continuous Self-Controlled Case Series
|Zhaobin Kuang, James Thomson, Michael Caldwell, Peggy Peissig, Ron Stewart, David Page
|Leveraging the patient-level temporal ordering information between numeric physiological measurements and various drug prescriptions provided in Electronic Health Records (EHRs), we propose a Continuous Self-controlled Case Series (CSCCS) model for CDR.
|How to Get Them a Dream Job?: Entity-Aware Features for Personalized Job Search Ranking
|Jia Li, Dhruv Arya, Viet Ha-Thuc, Shakti Sinha
|This paper proposes an approach to applying standardized entity data to improve job search quality and to make search results more personalized.
|Scalable Fast Rank-1 Dictionary Learning for fMRI Big Data Analysis
|Xiang Li, Milad Makkie, Binbin Lin, Mojtaba Sedigh Fazli, Ian Davidson, Jieping Ye, Tianming Liu, Shannon Quinn
|Thus in this work, we propose a novel distributed rank-1 dictionary learning (D-r1DL) model and apply it for fMRI big data analysis.
|CompanyDepot: Employer Name Normalization in the Online Recruitment Industry
|Qiaoling Liu, Faizan Javed, Matt Mcnair
|In this paper, we focus on this employer name normalization task, which has several unique challenges: handling employer names from both job postings and resumes, leveraging the corresponding location context, and handling name variations, irrelevant input data, and noises in the KB.
|Understanding Behaviors that Lead to Purchasing: A Case Study of Pinterest
|Caroline Lo, Dan Frankowski, Jure Leskovec
|In this paper we study user activity and purchasing behavior with the goal of building models of time-varying user purchasing intent.
|Images Don’t Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank
|Corey Lynch, Kamelia Aryafar, Josh Attenberg
|In this paper, we introduce a multimodal learning to rank model that combines these traditional features with visual semantic features transferred from a deep convolutional neural network.
|Text Mining in Clinical Domain: Dealing with Noise
|Hoang Nguyen, Jon Patrick
|This paper introduces a general clinical data mining architecture which is potential of addressing all of these challenges using: automatic proof-reading process, trainable finite state pattern recogniser, iterative model development and active learning.
|Detecting Devastating Diseases in Search Logs
|John Paparrizos, Ryen W. White, Eric Horvitz
|We identify searchers who issue credible, first-person diagnostic queries for pancreatic cancer and we learn models from prior search histories that predict which searchers will later input such queries.
|When Recommendation Goes Wrong: Anomalous Link Discovery in Recommendation Networks
|Bryan Perozzi, Michael Schueppert, Jack Saalweachter, Mayur Thakur
|We present a secondary ranking system to find and remove erroneous suggestions from a geospatial recommendation system.
|Deploying Analytics with the Portable Format for Analytics (PFA)
|Jim Pivarski, Collin Bennett, Robert L. Grossman
|We introduce a new language for deploying analytic models into products, services and operational systems called the Portable Format for Analytics (PFA).
|Singapore in Motion: Insights on Public Transport Service Level Through Farecard and Mobile Data Analytics
|Hasan Poonawala, Vinay Kolar, Sebastien Blandin, Laura Wynter, Sambit Sahu
|We thus propose a joint telco-and-farecard-based learning approach to understanding urban mobility.
|EMBERS AutoGSR: Automated Coding of Civil Unrest Events
|Parang Saraf, Naren Ramakrishnan
|We describe the EMBERS AutoGSR system that conducts automated coding of civil unrest events from news articles published in multiple languages.
|Compute Job Memory Recommender System Using Machine Learning
|Taraneh Taghavi, Maria Lupetini, Yaron Kretchmer
|In this paper, we explored a suite of statistical and machine learning techniques to develop a Compute Memory Recommender System for the Qualcomm chip design process with over 90% accuracy in predicting the amount of memory a job needs.
|Scalable Time-Decaying Adaptive Prediction Algorithm
|Yinyan Tan, Zhe Fan, Guilin Li, Fangshan Wang, Zhengbing Li, Shikai Liu, Qiuling Pan, Eric P. Xing, Qirong Ho
|Under this observation, we thereby propose a novel time-decaying online learning algorithm derived from the state-of-the-art FTRL-proximal algorithm, called Time-Decaying Adaptive Prediction (TDAP) algorithm.
|Analyzing Volleyball Match Data from the 2014 World Championships Using Machine Learning Techniques
|Jan Van Haaren, Horesh Ben Shitrit, Jesse Davis, Pascal Fua
|This paper proposes a relational-learning based approach for discovering strategies in volleyball matches based on optical tracking data.
|Crime Rate Inference with Big Data
|Hongjian Wang, Daniel Kifer, Corina Graif, Zhenhui Li
|In this paper, we used large-scale Point-Of-Interest data and taxi flow data in the city of Chicago, IL in the USA.
|Improving the Sensitivity of Online Controlled Experiments: Case Studies at Netflix
|Huizhi Xie, Juliette Aurisset
|We describe an innovative implementation of stratified sampling at Netflix where users are assigned to experiments in real time and discuss some surprising challenges with the implementation.
|Talent Circle Detection in Job Transition Networks
|Huang Xu, Zhiwen Yu, Jingyuan Yang, Hui Xiong, Hengshu Zhu
|Therefore, in this paper, we propose to create a job transition network where vertices stand for organizations and a directed edge represents the talent flow between two organizations for a time period.
|Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising
|Weinan Zhang, Tianxiong Zhou, Jun Wang, Jian Xu
|In this paper, we formulate CTR estimation and bid optimisation under such censored auction data.
|Compact and Scalable Graph Neighborhood Sketching
|Takuya Akiba, Yosuke Yano
|In the present study, we address this issue by designing a new graph sketching scheme, namely, sketch retrieval shortcuts (SRS).
|Streaming-LDA: A Copula-based Approach to Modeling Topic Dependencies in Document Streams
|Hesam Amoualian, Marianne Clausel, Eric Gaussier, Massih-Reza Amini
|We propose in this paper two new models for modeling topic and word-topic dependencies between consecutive documents in document streams.
|Assessing Human Error Against a Benchmark of Perfection
|Ashton Anderson, Jon Kleinberg, Sendhil Mullainathan
|An increasing number of domains are providing us with detailed trace data on human decisions in settings where we can evaluate the quality of these decisions via an algorithm.
|Inferring Network Effects from Observational Data
|David Arbour, Dan Garant, David Jensen
|We present Relational Covariate Adjustment (RCA), a general method for estimating causal effects in relational data.
|Communication Efficient Distributed Kernel Principal Component Analysis
|Maria Florina Balcan, Yingyu Liang, Le Song, David Woodruff, Bo Xie
|In this paper, we give an affirmative answer to the question by developing a communication efficient algorithm to perform kernel PCA in the distributed setting.
|Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns
|Roel Bertens, Jilles Vreeken, Arno Siebes
|To discover high-quality pattern sets directly from data, we introduce Ditto, a highly efficient algorithm that approximates the ideal result very well.
|The Limits of Popularity-Based Recommendations, and the Role of Social Ties
|Marco Bressan, Stefano Leucci, Alessandro Panconesi, Prabhakar Raghavan, Erisa Terolli
|In this paper we introduce a mathematical model that captures some of the salient features of recommender systems that are based on popularity and that try to exploit social ties among the users.
|Positive-Unlabeled Learning in Streaming Networks
|Shiyu Chang, Yang Zhang, Jiliang Tang, Dawei Yin, Yi Chang, Mark A. Hasegawa-Johnson, Thomas S. Huang
|In this paper, a principled probabilistic approach SPU is proposed to leverage the characteristics of the streaming PU inputs.
|FASCINATE: Fast Cross-Layer Dependency Inference on Multi-layered Networks
|Chen Chen, Hanghang Tong, Lei Xie, Lei Ying, Qing He
|In this paper, we address the problem of inferring the missing cross-layer dependencies on multi-layered networks.
|Predicting Matchups and Preferences in Context
|Shuo Chen, Thorsten Joachims
|We present a general probabilistic framework for predicting the outcome of pairwise matchups (e.g. two-player sport matches) and pairwise preferences (e.g. product preferences), both of which have widespread applications ranging from matchmaking in computer games to recommendation in e-commerce.
|XGBoost: A Scalable Tree Boosting System
|Tianqi Chen, Carlos Guestrin
|We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning.
|Robust Influence Maximization
|Wei Chen, Tian Lin, Zihan Tan, Mingfei Zhao, Xuren Zhou
|In this paper, we address the important issue of uncertainty in the edge influence probability estimates for the well studied influence maximization problem — the task of finding k seed nodes in a social network to maximize the influence spread. We propose the problem of robust influence maximization, which maximizes the worst-case ratio between the influence spread of the chosen seed set and the optimal seed set, given the uncertainty of the parameter input.
|Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations
|Wei Cheng, Kai Zhang, Haifeng Chen, Guofei Jiang, Zhengzhang Chen, Wei Wang
|To address these limitations, in this paper we propose a network diffusion based framework to identify significant causal anomalies and rank them.
|Towards Conversational Recommender Systems
|Konstantina Christakopoulou, Filip Radlinski, Katja Hofmann
|The goal of this paper is to begin to reduce this gap.
|TRIÈST: Counting Local and Global Triangles in Fully-Dynamic Streams with Fixed Memory Size
|Lorenzo De Stefani, Alessandro Epasto, Matteo Riondato, Eli Upfal
|We present TRIEST, a suite of one-pass streaming algorithms to compute unbiased, low-variance, high-quality approximations of the global and local (i.e., incident to each vertex) number of triangles in a fully-dynamic graph represented as an adversarial stream of edge insertions and deletions.
|A Subsequence Interleaving Model for Sequential Pattern Mining
|Jaroslav Fowkes, Charles Sutton
|We present a novel subsequence interleaving model based on a probabilistic model of the sequence database, which allows us to search for the most compressing set of patterns without designing a specific encoding scheme.
|Efficient Frequent Directions Algorithm for Sparse Matrices
|Mina Ghashami, Edo Liberty, Jeff M. Phillips
|This paper describes Sparse Frequent Directions, a variant of Frequent Directions for sketching sparse matrices.
|node2vec: Scalable Feature Learning for Networks
|Aditya Grover, Jure Leskovec
|Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks.
|Generalized Hierarchical Sparse Model for Arbitrary-Order Interactive Antigenic Sites Identification in Flu Virus Data
|Lei Han, Yu Zhang, Xiu-Feng Wan, Tong Zhang
|In this paper, we propose a Generalized Hierarchical Sparse Model (GHSM) as a generalization of the HSM models to learn arbitrary-order interactions.
|Joint Community and Structural Hole Spanner Detection via Harmonic Modularity
|Lifang He, Chun-Ta Lu, Jiaqi Ma, Jianping Cao, Linlin Shen, Philip S. Yu
|In this paper, we propose a novel harmonic modularity method to tackle both tasks simultaneously.
|Robust Influence Maximization
|Xinran He, David Kempe
|We define a Robust Influence Maximization framework wherein an algorithm is presented with a set of influence functions, typically derived from different influence models or different parameter settings for the same model.
|FRAUDAR: Bounding Graph Fraud in the Face of Camouflage
|Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, Christos Faloutsos
|We propose FRAUDAR, an algorithm that (a) is camouflage-resistant, (b) provides upper bounds on the effectiveness of fraudsters, and (c) is effective in real-world data.
|Temporal Order-based First-Take-All Hashing for Fast Attention-Deficit-Hyperactive-Disorder Detection
|Hao Hu, Joey Velez-Ginorio, Guo-Jun Qi
|Inspired by this, we propose a novel First-Take-All (FTA) hashing framework to investigate the problem of fast ADHD subjects detection through the fMRI time-series of neuron activities.
|When Social Influence Meets Item Inference
|Hui-Ju Hung, Hong-Han Shuai, De-Nian Yang, Liang-Hao Huang, Wang-Chien Lee, Jian Pei, Ming-Syan Chen
|In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation).
|Privacy-preserving Class Ratio Estimation
|Arun Shankar Iyer, J. Saketha Nath, Sunita Sarawagi
|In this paper we present learning models for the class ratio estimation problem, which takes as input an unlabeled set of instances and predicts the proportions of instances in the set belonging to the different classes.
|Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications
|Himanshu Jain, Yashoteja Prabhu, Manik Varma
|This paper addresses the issue by developing propensity scored losses which: (a) prioritize predicting the few relevant labels over the large number of irrelevant ones; (b) do not erroneously treat missing labels as irrelevant but instead provide unbiased estimates of the true loss function even when ground truth labels go missing under arbitrary probabilistic label noise models; and (c) promote the accurate prediction of infrequently occurring, hard to predict, but rewarding tail labels.
|CatchTartan: Representing and Summarizing Dynamic Multicontextual Behaviors
|Meng Jiang, Christos Faloutsos, Jiawei Han
|In this paper, we represent the behavioral data as a two-level matrix (temporal-behaviors by dimensional-values) and propose a novel representation for behavioral summary called Tartan that includes a set of dimensions, the values in each dimension, a list of consecutive time slices and the behaviors in each slice.
|Smart Reply: Automated Response Suggestion for Email
|Anjuli Kannan, Karol Kurach, Sujith Ravi, Tobias Kaufmann, Andrew Tomkins, Balint Miklos, Greg Corrado, Laszlo Lukacs, Marina Ganea, Peter Young, Vivek Ramavajjala
|In this paper we propose and investigate a novel end-to-end method for automatically generating short email responses, called Smart Reply.
|Mining Subgroups with Exceptional Transition Behavior
|Florian Lemmerich, Martin Becker, Philipp Singer, Denis Helic, Andreas Hotho, Markus Strohmaier
|We present a new method for detecting interpretable subgroups with exceptional transition behavior in sequential data.
|Point-of-Interest Recommendations: Learning Potential Check-ins from Friends
|Huayu Li, Yong Ge, Richang Hong, Hengshu Zhu
|To cope with these challenges, we define three types of friends (i.e., social friends, location friends, and neighboring friends) in LBSN, and develop a two-step framework to leverage the information of friends to improve POI recommendation accuracy and address cold-start problem.
|QUINT: On Query-Specific Optimal Networks
|Liangyue Li, Yuan Yao, Jie Tang, Wei Fan, Hanghang Tong
|A few recent works aim to further infer the optimal edge weights based on the side information.
|Dynamic Clustering of Streaming Short Documents
|Shangsong Liang, Emine Yilmaz, Evangelos Kanoulas
|In this paper, we consider the problem of dynamically clustering a streaming corpus of short documents.
|Rebalancing Bike Sharing Systems: A Multi-source Data Smart Optimization
|Junming Liu, Leilei Sun, Weiwei Chen, Hui Xiong
|To this end, in this paper, we develop a Meteorology Similarity Weighted K-Nearest-Neighbor (MSWK) regressor to predict the station pick-up demand based on large-scale historic trip records.
|Unified Point-of-Interest Recommendation with Temporal Interval Assessment
|Yanchi Liu, Chuanren Liu, Bin Liu, Meng Qu, Hui Xiong
|To this end, in this paper, we propose a unified recommender system, named the ‘Where and When to gO’ (WWO) recommender system, to integrate the user interests and their evolving sequential preferences with temporal interval assessment.
|AnyDBC: An Efficient Anytime Density-based Clustering Algorithm for Very Large Complex Datasets
|Son T. Mai, Ira Assent, Martin Storgaard
|In this paper, we propose a novel anytime approach to cope with this problem by reducing both the range query and the label propagation time of DBSCAN.
|Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphs
|Emaad Manzoor, Sadegh M. Milajerdi, Leman Akoglu
|We propose StreamSpot, a clustering based anomaly detection approach that addresses challenges in two key fronts: (1) heterogeneity, and (2) streaming nature.
|Regime Shifts in Streams: Real-time Forecasting of Co-evolving Time Sequences
|Yasuko Matsubara, Yasushi Sakurai
|In this paper, we present REGIMECAST, an efficient and effective method for forecasting co-evolving data streams.
|Skinny-dip: Clustering in a Sea of Noise
|Samuel Maurus, Claudia Plant
|In this paper we present SkinnyDip which, based on Hartigan’s elegant dip test of unimodality, represents an intriguing approach to clustering with an attractive set of properties.
|Semi-Markov Switching Vector Autoregressive Model-Based Anomaly Detection in Aviation Systems
|Igor Melnyk, Arindam Banerjee, Bryan Matthews, Nikunj Oza
|In this work we consider the problem of anomaly detection in heterogeneous, multivariate, variable-length time series datasets.
|Continuous Experience-aware Language Model
|Subhabrata Mukherjee, Stephan Günnemann, Gerhard Weikum
|This paper presents a new model that captures the continuous evolution of user experience, and the resulting language model in reviews and other posts.
|Structural Neighborhood Based Classification of Nodes in a Network
|Sharad Nandanwar, M. N. Murty
|In this paper, we propose a novel structural neighborhood-based classifier learning using a random walk.
|Modeling Precursors for Event Forecasting via Nested Multi-Instance Learning
|Yue Ning, Sathappan Muthiah, Huzefa Rangwala, Naren Ramakrishnan
|We develop a novel multiple instance learning based approach that jointly tackles the problem of identifying evidence-based precursors and forecasts events into the future.
|Asymmetric Transitivity Preserving Graph Embedding
|Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, Wenwu Zhu
|To tackle this challenge, we propose the idea of preserving asymmetric transitivity by approximating high-order proximity which are based on asymmetric transitivity.
|PTE: Enumerating Trillion Triangles On Distributed Systems
|Ha-Myung Park, Sung-Hyon Myaeng, U. Kang
|In this paper, we propose PTE (Pre-partitioned Triangle Enumeration), a new distributed algorithm for enumerating triangles in enormous graphs by resolving the structural inefficiency of the previous MapReduce algorithms.
|Robust Large-Scale Machine Learning in the Cloud
|Steffen Rendle, Dennis Fetterly, Eugene J. Shekita, Bor-yiing Su
|In this paper, we describe a new scalable coordinate descent (SCD) algorithm for generalized linear models whose convergence behavior is always the same, regardless of how much SCD is scaled out and regardless of the computing environment.
|"Why Should I Trust You?": Explaining the Predictions of Any Classifier
|Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
|In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally varound the prediction.
|ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages
|Matteo Riondato, Eli Upfal
|We present ABRA, a suite of algorithms to compute and maintain probabilistically-guaranteed, high-quality, approximations of the betweenness centrality of all nodes (or edges) on both static and fully dynamic graphs.
|Sampling of Attributed Networks from Hierarchical Generative Models
|Pablo Robles, Sebastian Moreno, Jennifer Neville
|In this paper, we propose a novel sampling method, CSAG, to sample from hierarchical GNMs and generate networks with correlated attributes.
|Goal-Directed Inductive Matrix Completion
|Si Si, Kai-Yang Chiang, Cho-Jui Hsieh, Nikhil Rao, Inderjit S. Dhillon
|In this paper, we propose Goal-directed Inductive Matrix Completion(GIMC) to learn a nonlinear mapping of the features so that they satisfy the required properties.
|Graph Wavelets via Sparse Cuts
|Arlei Silva, Xuan Hong Dang, Prithwish Basu, Ambuj Singh, Ananthram Swami
|In this paper, we study the problem of computing graph wavelet bases via sparse cuts in order to produce low-dimensional encodings of data-driven bases.
|Lexis: An Optimization Framework for Discovering the Hierarchical Structure of Sequential Data
|Payam Siyari, Bistra Dilkina, Constantine Dovrolis
|We propose a framework, referred to as Lexis, that produces an optimized hierarchical representation of a given set of "target" strings.
|Towards Optimal Cardinality Estimation of Unions and Intersections with Sketches
|We give new estimators for the cardinality of unions and intersection and show they approximate an optimal estimation procedure.
|Overcoming Key Weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity Measure
|Kai Ming Ting, Ye Zhu, Mark Carman, Yue Zhu, Zhi-Hua Zhou
|This paper introduces the first generic version of data dependent dissimilarity and shows that it provides a better closest match than distance measures for three existing algorithms in clustering, anomaly detection and multi-label classification.
|Just One More: Modeling Binge Watching Behavior
|William Trouleau, Azin Ashkan, Weicong Ding, Brian Eriksson
|In this paper, we introduce a novel statistical mixture model that incorporates these factors and presents a first of its kind characterization of viewer consumption behavior using a real-world dataset that includes playback data from a VOD service.
|Structural Deep Network Embedding
|Daixin Wang, Peng Cui, Wenwu Zhu
|To solve this problem, in this paper we propose a Structural Deep Network Embedding method, namely SDNE.
|Targeted Topic Modeling for Focused Analysis
|Shuai Wang, Zhiyuan Chen, Geli Fei, Bing Liu, Sherry Emery
|This paper studies this problem and proposes a novel targeted topic model (TTM) to enable focused analyses on any specific aspect of interest.
|Structured Doubly Stochastic Matrix for Graph Based Clustering: Structured Doubly Stochastic Matrix
|Xiaoqian Wang, Feiping Nie, Heng Huang
|To address this problem, in this paper, we propose a novel convex model to learn the structured doubly stochastic matrix by imposing low-rank constraint on the graph Laplacian matrix.
|A Multiple Test Correction for Streams and Cascades of Statistical Hypothesis Tests
|Geoffrey I. Webb, François Petitjean
|This paper introduces Subfamilywise Multiple Testing, a multiple-testing correction that can be used in applications for which there are repeated pools of null hypotheses from each of which a single null hypothesis is to be rejected and neither the specific hypotheses nor their number are known until the final rejection decision is completed.
|Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability
|Lingfei Wu, Ian E.H. Yen, Jie Chen, Rui Yan
|In this work, we observe that the RB features, with right choice of optimization solver, could be orders-of-magnitude more efficient than other random features and kernel approximation methods under the same requirement of accuracy.
|Robust Extreme Multi-label Learning
|Chang Xu, Dacheng Tao, Chao Xu
|The divide-and-conquer optimization technique is employed to increase the scalability of the proposed algorithm while theoretically guaranteeing its performance.
|Taxi Driving Behavior Analysis in Latent Vehicle-to-Vehicle Networks: A Social Influence Perspective
|Tong Xu, Hengshu Zhu, Xiangyu Zhao, Qi Liu, Hao Zhong, Enhong Chen, Hui Xiong
|To that end, in this paper, we propose a comprehensive study to reveal how the social propagation affects for better prediction of cab drivers’ future behaviors.
|DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks
|Shuangfei Zhai, Keng-hao Chang, Ruofei Zhang, Zhongfei Mark Zhang
|In this paper, we investigate the use of recurrent neural networks (RNNs) in the context of search-based online advertising.
|GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media
|Chao Zhang, Keyang Zhang, Quan Yuan, Luming Zhang, Tim Hanratty, Jiawei Han
|We propose GMove, a group-level mobility modeling method using GeoSM data.
|Approximate Personalized PageRank on Dynamic Graphs
|Hongyang Zhang, Peter Lofgren, Ashish Goel
|We propose and analyze two algorithms for maintaining approximate Personalized PageRank (PPR) vectors on a dynamic graph, where edges are added or deleted.
|Annealed Sparsity via Adaptive and Dynamic Shrinking
|Kai Zhang, Shandian Zhe, Chaoran Cheng, Zhi Wei, Zhengzhang Chen, Haifeng Chen, Guofei Jiang, Yuan Qi, Jieping Ye
|In this paper, inspired by the annealing technique in material science, we propose to achieve "annealed sparsity" by designing a dynamic shrinking scheme that simultaneously optimizes the regularization weights and model coefficients in sparse (multi-task) learning.
|Partial Label Learning via Feature-Aware Disambiguation
|Min-Ling Zhang, Bin-Bin Zhou, Xu-Ying Liu
|In this paper, a novel two-stage approach is proposed to learning from partial label examples based on feature-aware disambiguation.
|FINAL: Fast Attributed Network Alignment
|Si Zhang, Hanghang Tong
|In this paper, we propose a family of algorithms FINAL to align attributed networks.
|Come-and-Go Patterns of Group Evolution: A Dynamic Model
|Tianyang Zhang, Peng Cui, Christos Faloutsos, Yunfei Lu, Hao Ye, Wenwu Zhu, Shiqiang Yang
|In this paper, we examine temporal evolution patterns of more than 100 thousands social groups with more than 10 million users.
|NetCycle: Collective Evolution Inference in Heterogeneous Information Networks
|Yizhou Zhang, Yun Xiong, Xiangnan Kong, Yangyong Zhu
|In this paper, we study the problem of collective evolution inference, where the goal is to predict the values of the response variables for a group of related instances at the end of their life cycles.
|Accelerating Online CP Decompositions for Higher Order Tensors
|Shuo Zhou, Nguyen Xuan Vinh, James Bailey, Yunzhe Jia, Ian Davidson
|To fill this gap, we propose an efficient online algorithm that can incrementally track the CP decompositions of dynamic tensors with an arbitrary number of dimensions.
|Optimal Reserve Prices in Upstream Auctions: Empirical Application on Online Video Advertising
|Miguel Angel Alcobendas Lisbona, Sheide Chammas, Kuang-chih Lee
|We consider optimal reserve prices in BrightRoll Video Exchange when the inventory opportunity comes from other exchanges (downstream marketplaces).
|Burstiness Scale: A Parsimonious Model for Characterizing Random Series of Events
|Rodrigo Augusto da Silva Alves, Renato Martins Assuncao, Pedro Olmo Stancioli Vaz de Melo
|In this paper we propose a parsimonious way to characterize general RSEs, namely the Burstiness Scale (BuSca) model.
|MANTRA: A Scalable Approach to Mining Temporally Anomalous Sub-trajectories
|Prithu Banerjee, Pranali Yawalkar, Sayan Ranu
|In this paper, we study the problem of mining temporally anomalous sub-trajectory patterns from an input trajectory in a scalable manner.
|From Prediction to Action: A Closed-Loop Approach for Data-Guided Network Resource Allocation
|Yanan Bao, Huasen Wu, Xin Liu
|In this paper, moving beyond user experience prediction, we propose a closed-loop approach that uses data-generated prediction models to explicitly guide resource allocation for user experience improvement.
|Towards Robust and Versatile Causal Discovery for Business Applications
|Giorgos Borboudakis, Ioannis Tsamardinos
|ETIO is an instance of the logical approach to integrative causal discovery that has been relatively recently introduced and enables the solution of complex reverse-engineering problems in causal discovery.
|Deep Visual-Semantic Hashing for Cross-Modal Retrieval
|Yue Cao, Mingsheng Long, Jianmin Wang, Qiang Yang, Philip S. Yu
|This paper presents a new Deep Visual-Semantic Hashing (DVSH) model that generates compact hash codes of images and sentences in an end-to-end deep learning architecture, which capture the intrinsic cross-modal correspondences between visual data and natural language.
|Predicting Socio-Economic Indicators using News Events
|Sunandan Chakraborty, Ashwin Venkataraman, Srikanth Jagabathula, Lakshminarayanan Subramanian
|In this paper, we propose a novel generative model of real-world events and employ it to extract events from a large corpus of news articles.
|City-Scale Map Creation and Updating using GPS Collections
|Chen Chen, Cewu Lu, Qixing Huang, Qiang Yang, Dimitrios Gunopulos, Leonidas Guibas
|In this paper, we present a framework to create up-to-date maps with rich knowledge from GPS trajectory collections.
|Compressing Convolutional Neural Networks in the Frequency Domain
|Wenlin Chen, James Wilson, Stephen Tyree, Kilian Q. Weinberger, Yixin Chen
|In this paper, we present a novel net- work architecture, Frequency-Sensitive Hashed Nets (FreshNets), which exploits inherent redundancy in both convolutional layers and fully-connected layers of a deep learning model, leading to dramatic savings in memory and storage consumption.
|Parallel Dual Coordinate Descent Method for Large-scale Linear Classification in Multi-core Environments
|Wei-Lin Chiang, Mu-Chu Lee, Chih-Jen Lin
|In this work, we target at the parallelization in a multi-core environment.
|Multi-layer Representation Learning for Medical Concepts
|Edward Choi, Mohammad Taha Bahadori, Elizabeth Searles, Catherine Coffey, Michael Thompson, James Bost, Javier Tejedor-Sojo, Jimeng Sun
|In this work, we propose Med2Vec, which not only learns the representations for both medical codes and visits from large EHR datasets with over million visits, but also allows us to interpret the learned representations confirmed positively by clinical experts.
|Finding Gangs in War from Signed Networks
|Lingyang Chu, Zhefeng Wang, Jian Pei, Jiannan Wang, Zijin Zhao, Enhong Chen
|We model k-OCG finding task as a quadratic optimization problem.
|Efficient Processing of Network Proximity Queries via Chebyshev Acceleration
|Mustafa Coskun, Ananth Grama, Mehmet Koyuturk
|In this paper, we present an alternate approach to acceleration of network proximity queries using Chebyshev polynomials.
|Latent Space Model for Road Networks to Predict Time-Varying Traffic
|Dingxiong Deng, Cyrus Shahabi, Ugur Demiryurek, Linhong Zhu, Rose Yu, Yan Liu
|In this paper, we propose a Latent Space Model for Road Networks (LSM-RN) to address these challenges holistically.
|Compressing Graphs and Indexes with Recursive Graph Bisection
|Laxman Dhulipala, Igor Kabiljo, Brian Karrer, Giuseppe Ottaviano, Sergey Pupyrev, Alon Shalita
|We design and implement a novel theoretically sound reordering algorithm that is based on recursive graph bisection.
|Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test
|Denis Moreira dos Reis, Peter Flach, Stan Matwin, Gustavo Batista
|Therefore, most of the research on data stream classification focuses on proposing efficient models that can adapt to concept drifts and maintain a stable performance over time.
|Recurrent Marked Temporal Point Processes: Embedding Event History to Vector
|Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez, Le Song
|In this paper, we propose the Recurrent Marked Temporal Point Process (RMTPP) to simultaneously model the event timings and the markers.
|Learning Cumulatively to Become More Knowledgeable
|Geli Fei, Shuai Wang, Bing Liu
|In this paper, we propose to study a new problem, i.e., building a learning system that learns cumulatively.
|Squish: Near-Optimal Compression for Archival of Relational Datasets
|Yihan Gao, Aditya Parameswaran
|We study compression algorithms that leverage the relational structure to compress datasets to a much greater extent.
|Fast Component Pursuit for Large-Scale Inverse Covariance Estimation
|Lei Han, Yu Zhang, Tong Zhang
|Specifically, we propose an efficient COmponent Pursuit (COP) method to obtain the low-rank part, where each component can be sparse.
|Meta Structure: Computing Relevance in Large Heterogeneous Information Networks
|Zhipeng Huang, Yudian Zheng, Reynold Cheng, Yizhou Sun, Nikos Mamoulis, Xiang Li
|In this paper, we propose to use meta structure, which is a directed acyclic graph of object types with edge types connecting in between, to measure the proximity between objects.
|Robust and Effective Metric Learning Using Capped Trace Norm: Metric Learning via Capped Trace Norm
|Zhouyuan Huo, Feiping Nie, Heng Huang
|In this paper, we introduce a novel metric learning model using the capped trace norm based regularization, which uses a singular value threshold to constraint the metric matrix M as low-rank explicitly such that the rank of matrix M is stable when the large singular values vary.
|Subjectively Interesting Component Analysis: Data Projections that Contrast with Prior Expectations
|Bo Kang, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie
|We introduce a new method called Subjectively Interesting Component Analysis (SICA), designed to find data projections that are subjectively interesting, i.e, projections that truly surprise the end-user.
|Online Optimization Methods for the Quantification Problem
|Purushottam Kar, Shuai Li, Harikrishna Narasimhan, Sanjay Chawla, Fabrizio Sebastiani
|In this paper we propose the first online stochastic algorithms for directly optimizing these quantification-specific performance measures.
|Smart Broadcasting: Do You Want to be Seen?
|Mohammad Reza Karimi, Erfan Tavakoli, Mehrdad Farajtabar, Le Song, Manuel Gomez Rodriguez
|In this paper, we study the problem of smart broadcasting using the framework of temporal point processes, where we model users feeds and posts as discrete events occurring in continuous time.
|How to Compete Online for News Audience: Modeling Words that Attract Clicks
|Joon Hee Kim, Amin Mantrach, Alejandro Jaimes, Alice Oh
|In this paper, we conduct a large-scale analysis and modeling of 150K news articles published over a period of four months on the Yahoo home page.
|Causal Clustering for 1-Factor Measurement Models
|Erich Kummerfeld, Joseph Ramsey
|We present a provably correct novel algorithm, FindOneFactorClusters (FOFC), for solving this inference problem.
|Optimally Discriminative Choice Sets in Discrete Choice Models: Application to Data-Driven Test Design
|Igor Labutov, Frans Schalekamp, Kelvin Luu, Hod Lipson, Christoph Studer
|In this work, we (i) develop a multinomial-logit discrete choice model for the setting of MC testing, (ii) derive an optimization objective for selecting optimally discriminative option sets, (iii) propose an algorithm for finding a globally-optimal solution, and (iv) demonstrate the effectiveness of our approach via synthetic experiments and a user study.
|Interpretable Decision Sets: A Joint Framework for Description and Prediction
|Himabindu Lakkaraju, Stephen H. Bach, Jure Leskovec
|Here we propose interpretable decision sets, a framework for building predictive models that are highly accurate, yet also highly interpretable.
|Lightweight Monitoring of Distributed Streams
|Arnon Lazerson, Daniel Keren, Assaf Schuster
|Here we propose a very different approach, designated CB (for Convex/Concave Bounds).
|Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts
|Benjamin Letham, Lydia M. Letham, Cynthia Rudin
|Here we develop a Bayesian hierarchical model for inferring the underlying customer arrival rate and choice model from sales transaction data and the corresponding stock levels.
|Parallel Lasso Screening for Big Data Optimization
|Qingyang Li, Shuang Qiu, Shuiwang Ji, Paul M. Thompson, Jieping Ye, Jie Wang
|In this paper, we propose a novel parallel framework by parallelizing screening methods and integrating it with our proposed parallel solver.
|A Multi-Task Learning Formulation for Survival Analysis
|Yan Li, Jie Wang, Jieping Ye, Chandan K. Reddy
|To overcome the weaknesses of these two types of methods, in this paper, we reformulate the survival analysis problem as a multi-task learning problem and propose a new multi-task learning based formulation to predict the survival time by estimating the survival status at each time interval during the study duration.
|A Real Linear and Parallel Multiple Longest Common Subsequences (MLCS) Algorithm
|Yanni Li, Hui Li, Tihua Duan, Sheng Wang, Zhi Wang, Yang Cheng
|In this paper, we first unveil the fact that the state-of-the-art MLCS algorithms are unable to be applied to long and large-scale sequences alignments.
|Multi-Task Feature Interaction Learning
|Kaixiang Lin, Jianpeng Xu, Inci M. Baytas, Shuiwang Ji, Jiayu Zhou
|In this paper, we proposed a novel Multi-Task feature Interaction Learning~(MTIL) framework to exploit the task relatedness from high-order feature interactions, which provides better generalization performance by inductive transfer among tasks via shared representations of feature interactions.
|Infinite Ensemble for Image Clustering
|Hongfu Liu, Ming Shao, Sheng Li, Yun Fu
|In light of this, we propose the Infinite Ensemble Clustering (IEC), which incorporates the power of deep representation and ensemble clustering in a one-step framework to fuse infinite basic partitions.
|Scalable Pattern Matching over Compressed Graphs via Dedensification
|Antonio Maccioni, Daniel J. Abadi
|In this paper we present a dedensification technique that losslessly compresses the neighborhood around high-degree nodes.
|Scalable Betweenness Centrality Maximization via Sampling
|Ahmad Mahmoody, Charalampos E. Tsourakakis, Eli Upfal
|In this paper, we study the Centrality Maximization problem (CMP): given a graph G = (V,E) and a positive integer k, find a set S* ⊆ V that maximizes BWC subject to the cardinality constraint |S*| ≤ k.
|User Identity Linkage by Latent User Space Modelling
|Xin Mu, Feida Zhu, Ee-Peng Lim, Jing Xiao, Jianzong Wang, Zhi-Hua Zhou
|In this paper, we explore a new concept of “Latent User Space” to more naturally model the relationship between the underlying real users and their observed projections onto the varied social platforms, such that the more similar the real users, the closer their profiles in the latent user space.
|Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining
|Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama, Koji Tsuda, Ichiro Takeuchi
|In this paper we study predictive pattern mining problems where the goal is to construct a predictive model based on a subset of predictive patterns in the database.
|Predict Risk of Relapse for Patients with Multiple Stages of Treatment of Depression
|Zhi Nie, Pinghua Gong, Jieping Ye
|In this paper, we present a censored regression approach with a truncated $l_1$ loss function that can handle the uncertainty of relapse time.
|Lossless Separation of Web Pages into Layout Code and Data
|Adi Omari, Benny Kimelfeld, Eran Yahav, Sharon Shoham
|In this paper, we consider the opposite task: separating a given web page into a data component and a layout program.
|Unbounded Human Learning: Optimal Scheduling for Spaced Repetition
|Siddharth Reddy, Igor Labutov, Siddhartha Banerjee, Thorsten Joachims
|In the study of human learning, there is broad evidence that our ability to retain information improves with repeated exposure and decays with delay since last exposure.
|Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
|Xiang Ren, Wenqi He, Meng Qu, Clare R. Voss, Heng Ji, Jiawei Han
|We propose a general framework, called PLE, to jointly embed entity mentions, text features and entity types into the same low-dimensional space where, in that space, objects whose types are semantically close have similar representations.
|Reconstructing an Epidemic Over Time
|Polina Rozenshtein, Aristides Gionis, B. Aditya Prakash, Jilles Vreeken
|We consider the problem of reconstructing an epidemic over time, or, more general, reconstructing the propagation of an activity in a network.
|Improving Survey Aggregation with Sparsely Represented Signals
|Tianlin Shi, Forest Agostinelli, Matthew Staib, David Wipf, Thomas Moscibroda
|In this paper, we develop a new aggregation technique to reduce the cost of surveying.
|Dynamics of Large Multi-View Social Networks: Synergy, Cannibalization and Cross-View Interplay
|Yu Shi, Myunghwan Kim, Shaunak Chatterjee, Mitul Tiwari, Souvik Ghosh, Rómer Rosales
|In this paper, we propose approaches to capture and analyze multi-view network dynamics from various aspects.
|Data-driven Automatic Treatment Regimen Development and Recommendation
|Leilei Sun, Chuanren Liu, Chonghui Guo, Hui Xiong, Yanming Xie
|In this paper, we aim at exploiting the rich information in doctor orders and developing data-driven approaches for improving clinical treatments.
|Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices
|Yasuo Tabei, Hiroto Saigo, Yoshihiro Yamanishi, Simon J. Puglisi
|In this paper we address this need by presenting a scalable algorithm for partial least squares regression (PLS), which we call compression-based PLS (cPLS), to learn predictive linear models with a high interpretability from massive high-dimensional data.
|From Truth Discovery to Trustworthy Opinion Discovery: An Uncertainty-Aware Quantitative Modeling Approach
|Mengting Wan, Xiangyu Chen, Lance Kaplan, Jiawei Han, Jing Gao, Bo Zhao
|In this study, we focus on the quantitative opinion, propose an uncertainty-aware approach called Kernel Density Estimation from Multiple Sources (KDEm) to estimate its probability distribution, and summarize trustworthy information based on this distribution.
|The Million Domain Challenge: Broadcast Email Prioritization by Cross-domain Recommendation
|Beidou Wang, Martin Ester, Yikang Liao, Jiajun Bu, Yu Zhu, Ziyu Guan, Deng Cai
|In this work, we propose the first broadcast email prioritization framework considering large numbers of mailing lists by formulating this problem as a cross domain recommendation problem.
|Transfer Knowledge between Cities
|Ying Wei, Yu Zheng, Qiang Yang
|In this paper, we propose a FLexible multimOdal tRAnsfer Learning (FLORAL) method to transfer knowledge from a city where there exist sufficient multimodal data and labels, to this kind of cities to fully alleviate the two problems.
|Probabilistic Robust Route Recovery with Spatio-Temporal Dynamics
|Hao Wu, Jiangyun Mao, Weiwei Sun, Baihua Zheng, Hanyuan Zhang, Ziyang Chen, Wei Wang
|In this paper, we study the problem of recovering the entire route between two distant consecutive locations in a trajectory.
|A Truth Discovery Approach with Theoretical Guarantee
|Houping Xiao, Jing Gao, Zhaoran Wang, Shiyu Wang, Lu Su, Han Liu
|In contrast, in this paper we propose a truth discovery approach with theoretical guarantee.
|Towards Confidence in the Truth: A Bootstrapping based Truth Discovery Approach
|Houping Xiao, Jing Gao, Qi Li, Fenglong Ma, Lu Su, Yunlong Feng, Aidong Zhang
|To address this challenge, in this paper, we propose a novel truth discovery method (ETCIBoot) to construct confidence interval estimates as well as identify truths, where the bootstrapping techniques are nicely integrated into the truth discovery procedure.
|Online Feature Selection: A Limited-Memory Substitution Algorithm and Its Asynchronous Parallel Variation
|Haichuan Yang, Ryohei Fujimaki, Yukitaka Kusumura, Ji Liu
|To overcome these two drawbacks, this paper proposes a limited-memory and model parameter free online feature selection algorithm, namely online substitution (OS) algorithm.
|Absolute Fused Lasso and Its Application to Genome-Wide Association Studies
|Tao Yang, Jun Liu, Pinghua Gong, Ruiwen Zhang, Xiaotong Shen, Jieping Ye
|In this paper, we consider a regularized model which can simultaneously identify important features and group similar features together.
|Diversified Temporal Subgraph Pattern Mining
|Yi Yang, Da Yan, Huanhuan Wu, James Cheng, Shuigeng Zhou, John C.S. Lui
|In this paper, we study the problem of mining a set of diversified temporal subgraph patterns from a temporal graph, where each subgraph is associated with the time interval that the pattern spans.
|Distributing the Stochastic Gradient Sampler for Large-Scale LDA
|Yuan Yang, Jianfei Chen, Jun Zhu
|In this paper, we present embarrassingly parallel SGLD (EPSGLD), a novel distributed stochastic gradient sampling method for topic models.
|FUSE: Full Spectral Clustering
|Wei Ye, Sebastian Goebl, Claudia Plant, Christian Böhm
|Thus, in this paper, we exploit the fusion of the cluster-separation information from all eigenvectors to achieve a better clustering result.
|A Text Clustering Algorithm Using an Online Clustering Scheme for Initialization
|Jianhua Yin, Jianyong Wang
|In this paper, we propose a text clustering algorithm using an online clustering scheme for initialization called FGSDMM+.
|Convex Optimization for Linear Query Processing under Approximate Differential Privacy
|Ganzhao Yuan, Yin Yang, Zhenjie Zhang, Zhifeng Hao
|Hence, in the past much effort has been devoted in solving this non-convex optimization program.
|Beyond Sigmoids: The NetTide Model for Social Network Growth, and Its Applications
|Chengxi Zang, Peng Cui, Christos Faloutsos
|In its place, we propose NETTIDE, along with differential equations for the growth of the count of nodes, as well as links.
|Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit
|Chunqiu Zeng, Qing Wang, Shekoofeh Mokhtari, Tao Li
|In this paper, we study the time varying contextual multi-armed problem where the reward mapping function changes over time.
|Accelerated Stochastic Block Coordinate Descent with Optimal Sampling
|Aston Zhang, Quanquan Gu
|We propose an accelerated stochastic block coordinate descent (ASBCD) algorithm, which incorporates the incrementally averaged partial derivative into the stochastic partial derivative and exploits optimal sampling.
|Collaborative Multi-View Denoising
|Lei Zhang, Shupeng Wang, Xiaoyu Zhang, Yong Wang, Binbin Li, Dinggang Shen, Shuiwang Ji
|To address this challenge, we propose a general framework to jointly denoise corrupted views in this paper.
|Online Asymmetric Active Learning with Imbalanced Data
|Xiaoxuan Zhang, Tianbao Yang, Padmini Srinivasan
|In particular, we propose an asymmetric active querying strategy that assigns different probabilities for query to examples predicted as positive and negative.
|FLASH: Fast Bayesian Optimization for Data Analytic Pipelines
|Yuyu Zhang, Mohammad Taha Bahadori, Hang Su, Jimeng Sun
|In this work, we propose Fast LineAr SearcH (FLASH), an efficient method for tuning analytic pipelines.
|Portfolio Selections in P2P Lending: A Multi-Objective Perspective
|Hongke Zhao, Qi Liu, Guifeng Wang, Yong Ge, Enhong Chen
|To that end, in this paper, we present a holistic study on portfolio selections in P2P lending.
|Hierarchical Incomplete Multi-source Feature Learning for Spatiotemporal Event Forecasting
|Liang Zhao, Jieping Ye, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan
|This paper proposes a novel feature learning model that concurrently addresses all the above challenges.
|Efficient Shift-Invariant Dictionary Learning
|Guoqing Zheng, Yiming Yang, Jaime Carbonell
|In this paper we propose a new framework for unsupervised discovery of both the shift-invariant basis and the sparse coding of input data, with efficient algorithms for tractable optimization.
|Topic Modeling of Short Texts: A Pseudo-Document View
|Yuan Zuo, Junjie Wu, Hui Zhang, Hao Lin, Fei Wang, Ke Xu, Hui Xiong
|In light of this, in this paper, we propose a novel probabilistic model called Pseudo-document-based Topic Model (PTM) for short text topic modeling.
|Scalable Data Analytics Using R: Single Machines to Hadoop Spark Clusters
|John-Mark Agosta, Debraj GuhaThakurta, Robert Horton, Mario Inchiosa, Srini Kumar, Mengyue Zhao
|In this tutorial we will discuss solutions that demonstrate the use of distributed compute environments and end to end solutions for R.
|Lifelong Machine Learning and Computer Reading the Web
|Zhiyuan Chen, Estevam R. Hruschka, Bing Liu
|In this tutorial, we introduce this important problem and the existing LML techniques and discuss opportunities and challenges of big data for lifelong machine learning.
|IoT Big Data Stream Mining
|Gianmarco De Francisci Morales, Albert Bifet, Latifur Khan, Joao Gama, Wei Fan
|IoT Big Data Stream Mining
|Mining Reliable Information from Passively and Actively Crowdsourced Data
|Jing Gao, Qi Li, Bo Zhao, Wei Fan, Jiawei Han
|To answer the need of a systematic introduction of the field and comparison of the techniques, we will present an organized picture on crowdsourcing methods in this tutorial.
|Ashish Gupta, Neera Agarwal
|This tutorial will provide overview of streaming systems and hands on tutorial on building streaming analytics systems using open source technologies.
|Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining
|Sara Hajian, Francesco Bonchi, Carlos Castillo
|The aim of this tutorial is to survey algorithmic bias, presenting its most common variants, with an emphasis on the algorithmic techniques and key ideas developed to derive efficient solutions.
|Collective Sensemaking via Social Sensors: Extracting, Profiling, Analyzing, and Predicting Real-world Events
|Yuheng Hu, Yu-Ru Lin, Jiebo Luo
|Collective Sensemaking via Social Sensors: Extracting, Profiling, Analyzing, and Predicting Real-world Events
|Extracting Optimal Performance from Dynamic Time Warping
|Abdullah Mueen, Eamonn Keogh
|In this tutorial, we correct these misunderstandings and we summarize the research efforts in optimizing both the efficiency and effectiveness of both the basic DTW algorithm, and of the higher-level algorithms that exploit DTW such as similarity search, clustering and classification.
|Scalable Learning of Graphical Models
|Francois Petitjean, Geoffrey I. Webb
|This tutorial covers the core building blocks that are necessary to build and use scalable graphical modeling technologies on large and high-dimensional data.
|Leveraging Propagation for Data Mining: Models, Algorithms and Applications
|B. Aditya Prakash, Naren Ramakrishnan
|This tutorial will cover recent and state-of-the-art research on how propagation-like processes can help big-data mining specifically involving large networks and time-series, algorithms behind network problems, and their practical applications in various diverse settings.
|CNTK: Microsoft’s Open-Source Deep-Learning Toolkit
|Frank Seide, Amit Agarwal
|This tutorial will introduce the Computational Network Toolkit, or CNTK, Microsoft’s cutting-edge open-source deep-learning toolkit for Windows and Linux.
|Healthcare Data Mining with Matrix Models
|Fei Wang, Ping Zhang, Joel Dudley
|In this tutorial, we provide a review of recent advances in algorithms and methods using matrix and their potential applications in biomedical informatics.
|Business Applications of Predictive Modeling at Scale
|Qiang Zhu, Songtao Guo, Paul Ogilvie, Yan Liu
|In this tutorial, we will focus on the best practice of predictive modeling in the big data era and its applications in industry, with motivating examples across a range of business tasks and relevance products.