Paper Digest: UAI 2025 Papers & Highlights

July 24, 2025January 28, 2026 admin

The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. 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 to quickly get the main idea of each paper.

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TABLE 1: Paper Digest: UAI 2025 Papers & Highlights

	Paper	Author(s)
1	Calibrated Regression Against An Adversary Without Regret Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our work seeks to achieve two goals: (1) producing valid probabilities that accurately reflect model confidence; (2) ensuring that traditional notions of performance (e.g., high accuracy) still hold. We introduce online algorithms guaranteed to achieve these goals on arbitrary streams of data-points, including data chosen by an adversary.	Shachi Deshpande; Charles Marx; Volodymyr Kuleshov;
2	On Information-Theoretic Measures of Predictive Uncertainty Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this work, we revisit core concepts to propose a framework for information-theoretic measures of predictive uncertainty.	Kajetan Schweighofer; Lukas Aichberger; Mykyta Ielanskyi; Sepp Hochreiter;
3	Hindsight Merging: Diverse Data Generation with Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This reduction in diversity is partly due to the optimization process, which theoretically decreases model entropy in exchange for task performance. To counteract this, we introduce hindsight merging, a technique that combines a fine-tuned model with a previous training checkpoint using linear interpolation to restore entropy and improve performance.	Veniamin Veselovsky; Benedikt Stroebl; Gianluca Bencomo; Dilip Arumugam; Lisa Schut; Arvind Narayanan; Thomas L. Griffiths;
4	Periodical Moving Average Accelerates Gradient Accumulation for Post-Training Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we reveal that the Exponential Moving Average (EMA) in momentum-based optimizers exponentially discounts historical gradients, thereby limiting their effectiveness in stabilizing parameter updates, especially during post-training when parameter drift is minimal.	Yumou Liu; An Li; Chaojie Li; Fei Yu; Benyou Wang;
5	RL, But Don�t Do Anything I Wouldn�t Do Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: All current cutting-edge language models are RL agents that are KL-regularized to a "base policy" that is purely predictive. Unfortunately, we demonstrate that when this base policy is a Bayesian predictive model of a trusted policy, the KL constraint is no longer reliable for controlling the behavior of an advanced RL agent.	Michael K. Cohen; Marcus Hutter; Yoshua Bengio; Stuart Russell;
6	Simulation-Free Differential Dynamics Through Neural Conservation Laws Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a novel simulation-free framework for training continuous-time diffusion processes over very general objective functions.	Mengjian Hua; Eric Vanden-Eijnden; Ricky T. Q. Chen;
7	Exploring Exploration in Bayesian Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This work introduces two novel approaches -observation traveling salesman distance and observation entropy- to quantify the exploration characteristics of acquisition functions based on their selected observations.	Leonard Papenmeier; Nuojin Cheng; Stephen Becker; Luigi Nardi;
8	Concept Forgetting Via Label Annealing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our goal is to develop techniques for forgetting specific undesired concepts from a pre-trained classification model’s prediction. To achieve this goal, we present an algorithm called Label ANnealing (LAN).	Subhodip Panda; Ananda Theertha Suresh; Atri Guha; Prathosh Ap;
9	Best Possible Q-Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the convergence and optimality of most decentralized algorithms are not theoretically guaranteed, since the transition probabilities are non-stationary as all agents are updating policies simultaneously. To tackle this challenge, we propose \textit{best possible operator}, a novel decentralized operator, and prove that the policies of cooperative agents will converge to the optimal joint policy if each agent independently updates its individual state-action value by the operator when there is only one optimal joint policy.	Jiechuan Jiang; Zongqing Lu;
10	Revisiting The Equivalence of Bayesian Neural Networks and Gaussian Processes: On The Importance of Learning Activations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Gaussian Processes (GPs) provide a convenient framework for specifying function-space priors, making them a natural choice for modeling uncertainty.	Marcin Sendera; Amin Sorkhei; Tomasz Kusmierczyk;
11	Can A Bayesian Oracle Prevent Harm from An Agent? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: With the long-term goal of obtaining a probabilistic guarantee that would apply in every context, we consider estimating a context-dependent bound on the probability of violating a given safety specification.	Yoshua Bengio; Michael K. Cohen; Nikolay Malkin; Matt MacDermott; Damiano Fornasiere; Pietro Greiner; Younesse Kaddar;
12	COS-DPO: Conditioned One-Shot Multi-Objective Fine-Tuning Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address the challenge, we propose a Conditioned One-Shot fine-tuning framework (COS-DPO) that extends the Direct Preference Optimization technique, originally developed for efficient LLM alignment with preference data, to accommodate the MOFT settings.	Yinuo Ren; Tesi Xiao; Michael Shavlovsky; Lexing Ying; Holakou Rahmanian;
13	Letting Uncertainty Guide Your Multimodal Machine Translation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, current approaches lack explicit mechanisms to quantify and manage the uncertainty during translation process, resulting in the utilization of image information being a black box. This makes it difficult to effectively address the issues of incomplete utilization of visual information and even potential degradation of translation quality when using visual information.To address these challenges, we introduce a novel Uncertainty-Guided Multimodal Machine Translation (UG-MMT) framework that redefines how translation systems handle ambiguity through systematic uncertainty reduction.	Wuyi Liu; Yue Gao; Yige Mao; Jing Zhao;
14	Just Trial Once: Ongoing Causal Validation of Machine Learning Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this work, we present an alternative solution: using only data from a prior RCT, we give conditions under which the causal impact of a new ML model can be precisely bounded or estimated, even if it was not included in the RCT.	Jacob M. Chen; Michael Oberst;
15	A Probabilistic Neuro-symbolic Layer for Algebraic Constraint Satisfaction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: Neural models struggle in the presence of these constraints, especially when they involve intricate algebraic relationships. To address this, we introduce a differentiable probabilistic layer that guarantees the satisfaction of non-convex algebraic constraints over continuous variables.	Leander Kurscheidt; Paolo Morettin; Roberto Sebastiani; Andrea Passerini; Antonio Vergari;
16	DyGMAE: A Novel Dynamic Graph Masked Autoencoder for Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, most existing DLP methods rely on local information, ignoring global information and failing to capture complex features in real-world dynamic graphs. To address these issues, we propose DyGMAE, a novel dynamic GMAE method specifically designed for DLP.	Weixiong Liu; Junwei Cheng; Zhongyu Pan; Chaobo He; Quanlong Guan;
17	NRFlow: Towards Noise-Robust Generative Modeling Via High-Order Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose NRFlow, a novel extension to flow-based generative modeling that incorporates second-order dynamics through acceleration fields.	Bo Chen; Chengyue Gong; Xiaoyu Li; Yingyu Liang; Zhizhou Sha; Zhenmei Shi; Zhao Song; Mingda Wan; Xugang Ye;
18	On Constant Regret for Low-Rank MDPs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although there exist instance-dependent regret bounds for linear Markov decision processes (MDPs) and low-rank bandits, extensions to low-rank MDPs remain unexplored. In this work, we close this gap and provide regret bounds for low-rank MDPs in an instance-dependent setting.	Alexander Sturm; Sebastian Tschiatschek;
19	ELBO, Regularized Maximum Likelihood, and Their Common One-sample Approximation for Training Stochastic Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We observe that the common one-sample approximation of the standard training objective can be viewed both as maximizing the Evidence Lower Bound (ELBO) and as maximizing a regularized log-likelihood of a compound distribution.	Sina D�ubener; Simon Damm; Asja Fischer;
20	Collaborative Prediction: To Join or To Disjoin Datasets Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we study the problem of developing practical algorithms that select appropriate dataset to minimize population loss of our prediction model with high probability.	Kyung Rok Kim; Yansong Wang; Xiaocheng Li; Guanting Chen;
21	Online Learning with Stochastically Partitioning Experts Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a suitably adapted version of the Hedge algorithm called Hedge-G, which uses a constant learning rate and has $O(\sqrt{2^d T \log T})$ expected regret, which is order-optimal.	Puranjay Datta; Sharayu Moharir; Jaya Prakash Champati;
22	MutualNeRF: Improve The Performance of NeRF Under Limited Samples with Mutual Information Theory Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces MutualNeRF, a framework enhancing Neural Radiance Field (NeRF) performance under limited samples using Mutual Information Theory.	Zifan Wang; Jingwei Li; Yitang Li; Yunze Liu;
23	A Quantum Information Theoretic Approach to Tractable Probabilistic Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: By recursively nesting sums and products, probabilistic circuits have emerged in recent years as an attractive class of generative models as they enjoy, for instance, polytime marginalization of random variables. In this work we study these machine learning models using the framework of quantum information theory, leading to the introduction of positive unital circuits (PUnCs), which generalize circuit evaluations over positive real-valued probabilities to circuit evaluations over positive semi-definite matrices.	Pedro Zuidberg Dos Martires;
24	Off-policy Predictive Control with Causal Sensitivity Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present an identification bound and propose an algorithm to account for hidden confounding during model-predictive control.	Myrl G Marmarelis; Ali Hasan; Kamyar Azizzadenesheli; R. Michael Alvarez; Anima Anandkumar;
25	Robust Optimization with Diffusion Models for Green Security Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Prior work has leveraged game theory to design robust patrol strategies to handle uncertainty, but existing adversarial behavior models primarily rely on Gaussian processes or linear models, which lack the expressiveness needed to capture intricate behavioral patterns. To address this limitation, we propose a conditional diffusion model for adversary behavior modeling, leveraging its strong distribution-fitting capabilities.	Lingkai Kong; Haichuan Wang; Yuqi Pan; Cheol Woo Kim; Mingxiao Song; Alayna Nguyen; Tonghan Wang; Haifeng Xu; Milind Tambe;
26	Conformal Prediction Sets for Deep Generative Models Via Reduction to Conformal Regression Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We consider the problem of generating valid and small prediction sets by sampling outputs (e.g., software code and natural language text) from a black-box deep generative model for a given input (e.g., textual prompt).	Hooman Shahrokhi; Devjeet Raj Roy; Yan Yan; Venera Arnaoudova; Jana Doppa;
27	DF$^2$: Distribution-Free Decision-Focused Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Gradient approximation error occurs when the objectives are non-convex and KKT conditions cannot be directly applied. In this paper, we present DF$^2$-the first \textit{distribution-free} decision-focused learning method designed to mitigate these three bottlenecks.	Lingkai Kong; Wenhao Mu; Jiaming Cui; Yuchen Zhuang; B. Aditya Prakash; Bo Dai; Chao Zhang;
28	Critical Influence of Overparameterization on Sharpness-aware Minimization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: Despite its contemporary relevance to overparameterization, however, this sharpness-aware minimization (SAM) strategy has not been studied much yet as to exactly how it is affected by overparameterization. In this work, we analyze SAM under varying degrees of overparameterization, presenting both empirical and theoretical findings that reveal its critical influence on SAM’s effectiveness.	Sungbin Shin; Dongyeop Lee; Maksym Andriushchenko; Namhoon Lee;
29	Do Vendi Scores Converge with Finite Samples? Truncated Vendi Score for Finite-Sample Convergence Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we investigate the statistical convergence of the Vendi and RKE scores under restricted sample sizes.	Azim Ospanov; Farzan Farnia;
30	Collapsing Sequence-Level Data-Policy Coverage Via Poisoning Attack in Offline Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing studies aim to improve data-policy coverage to mitigate distributional shifts, but overlook security risks from insufficient coverage, and the single-step analysis is not consistent with the multi-step decision-making nature of offline RL. To address this, we introduce the sequence-level concentrability coefficient to quantify coverage, and reveal its exponential amplification on the upper bound of estimation errors through theoretical analysis.	Xue Zhou; Dapeng Man; Chen Xu; Fanyi Zeng; Tao Liu; Huan Wang; Shucheng He; Chaoyang Gao; Wu Yang;
31	An Optimal Algorithm for Strongly Convex Min-Min Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The optimal accelerated gradient method of Yurii Nesterov achieves a convergence rate that requires approximately $ \mathcal{O}((\min(\mu_x, \mu_y))^{-1/2}) $ evaluations of the partial gradients $ \nabla_x f $ and $ \nabla_y f $. In this paper, we propose a novel optimization algorithm that improves upon this complexity by requiring only $ \mathcal{O}(\mu_x^{-1/2}) $ computations of $ \nabla_x f $ and $ \mathcal{O}(\mu_y^{-1/2}) $ computations of $ \nabla_y f $.	Dmitry Kovalev; Alexander Gasnikov; Grigory Malinovsky;
32	Dynamic Maintenance of Kernel Density Estimation Data Structure: From Practice to Theory Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we focus on the dynamic maintenance of KDE data structures with robustness to adversarial queries.	Jiehao Liang; Zhao Song; Zhaozhuo Xu; Junze Yin; Danyang Zhuo;
33	How Likely Are Two Voting Rules Different? Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We characterize the maximum likelihood that two voting rule outcomes are different and that the winner of one voting rule is the loser of another (implying that they are {\em drastically different}) on positional scoring rules, Condorcet winner/loser, Copeland, Ranked Pairs, and STV (Single Transferable Vote) under any fixed number of alternatives.	Ziqi Yu; Lirong Xia; Qishen Han; Chengkai Zhang;
34	Metric Learning in An RKHS Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper develops a general RKHS framework for metric learning and provides novel generalization guarantees and sample complexity bounds.	Gokcan Tatli; Yi Chen; Blake Mason; Robert D Nowak; Ramya Korlakai Vinayak;
35	Order-Optimal Global Convergence for Actor-Critic with General Policy and Neural Critic Parametrization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Natural Actor-Critic with Data Drop (NAC-DD) algorithm, which integrates Natural Policy Gradient methods with a Data Drop technique to mitigate statistical dependencies inherent in Markovian sampling.	Swetha Ganesh; Jiayu Chen; Washim Uddin Mondal; Vaneet Aggarwal;
36	Learning to Stabilize Unknown LTI Systems on A Single Trajectory Under Stochastic Noise Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We study the problem of learning to stabilize unknown noisy Linear Time-Invariant (LTI) systems on a single trajectory.	Ziyi Zhang; yorie nakahira; Guannan Qu;
37	An Information-theoretic Perspective of Hierarchical Clustering on Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The seminal work of \citep{dasgupta2016cost} has introduced a combinatorial cost function for hierarchical graph clustering that has inspired numerous follow-up studies adopting similar combinatorial approaches. In this paper, we investigate this problem from the \emph{information-theoretic} perspective.	Yicheng Pan; Bingchen Fan; Pengyu Long; Feng Zheng;
38	Proxy-informed Bayesian Transfer Learning with Unknown Sources Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: A challenging open problem is addressing the empirical phenomenon of negative transfer, whereby the transfer learner performs worse on the target data after taking the source data into account than before.	Sabina J. Sloman; Julien Martinelli; Samuel Kaski;
39	Near-Optimal Regret Bounds for Federated Multi-armed Bandits with Fully Distributed Communication Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we focus on the research of federated multi-armed bandit (FMAB) problems where agents can only communicate with their neighbors.	Haoran Zhang; Xuchuang Wang; Hao-Xu Chen; Hao Qiu; Lin Yang; Yang Gao;
40	VADIS: Investigating Inter-View Representation Biases for Multi-View Partial Multi-Label Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a novel MVPML method called \textsc{Vadis}, which investigates view-aware representations for disambiguation and predictive model learning.	Jie Wang; Ning Xu; Xin Geng;
41	Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we show that the evolution of time-varying generative models can be projected onto an exponential family manifold, naturally creating a link between the parameters of a generative model and those of a probabilistic model.	Song Liu; Leyang Wang; Yakun Wang;
42	Discriminative Ordering Through Ensemble Consensus Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this work, we take inspiration from consensus clustering and assume that a set of clustering models is able to uncover hidden structures in the data.	Louis Ohl; Fredrik Lindsten;
43	Testing Generalizability in Causal Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing methods often rely on arbitrary proxy predictive metrics like mean squared error, but do not directly answer whether a model can or cannot generalize. To address this gap in the domain of causal inference, we propose a systematic framework for statistically evaluating the generalizability of high-dimensional causal inference models.	Daniel de Vassimon Manela; Linying Yang; Robin J. Evans;
44	Tuning Algorithmic and Architectural Hyperparameters in Graph-Based Semi-Supervised Learning with Provable Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Graph-based semi-supervised learning is a powerful paradigm in machine learning for modeling and exploiting the underlying graph structure that captures the relationship between labeled and unlabeled data. A large number of classical as well as modern deep learning based algorithms have been proposed for this problem, often having tunable hyperparameters.	Ally Yalei Du; Eric Huang; Dravyansh Sharma;
45	Generative Uncertainty in Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Detecting such samples without human inspection remains a challenging task. To address this, we propose a Bayesian framework for estimating generative uncertainty of synthetic samples.	Metod Jazbec; Eliot Wong-Toi; Guoxuan Xia; Dan Zhang; Eric Nalisnick; Stephan Mandt;
46	Epistemic Uncertainty in Conformal Scores: A Unified Approach Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: We introduce $\texttt{EPICSCORE}$, a model-agnostic approach that enhances any conformal score by explicitly integrating epistemic uncertainty.	Luben Miguel Cruz Cabezas; Vagner Silva Santos; Thiago Ramos; Rafael Izbicki;
47	Probabilistic Semantics Guided Discovery of Approximate Functional Dependencies Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Most of existing methods for AFD discover are insufficient to balance the efficiency and accuracy due to the massive search space and permission of violations. To address these issues, we propose an efficient method of probabilistic semantics guided discovery of AFDs based on Bayesian network (BN).	Liang Duan; Xinran Wu; Xinhui Li; Lixing Yu; Kun Yue;
48	Residual Reweighted Conformal Prediction for Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While residual reweighting CP variants address some of these limitations, they neglect graph topology, cluster-specific uncertainties, and risk data leakage by reusing training sets. To address these issues, we propose Residual Reweighted GNN (RR-GNN), a framework designed to generate minimal prediction sets with provable marginal coverage guarantees.	Zheng Zhang; Jie Bao; Zhixin Zhou; nicolo colombo; Lixin Cheng; Rui Luo;
49	Flow-Based Delayed Hawkes Process Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a simple yet powerful extension: the Flow-based Delayed Hawkes Process, which integrates Normalizing Flows as a generative model to parameterize the Hawkes process.	Chao Yang; Wendi Ren; Shuang Li;
50	Creative Agents: Empowering Agents with Imagination for Creative Tasks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: We introduce several approaches to implementing the components of creative agents.	Penglin Cai; Chi Zhang; Yuhui Fu; Haoqi Yuan; Zongqing Lu;
51	Corruption-Robust Variance-aware Algorithms for Generalized Linear Bandits Under Heavy-tailed Rewards Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, real-world challenges such as heavy-tailed noise, reward corruption, and nonlinear reward functions remain difficult to address. To tackle these difficulties, we propose GAdaOFUL, a novel algorithm that leverages adaptive Huber regression to achieve robustness in generalized linear models (GLMs), where rewards can be nonlinear functions of features.	Qingyuan Yu; Euijin Baek; Xiang Li; Qiang Sun;
52	Improving Adversarial Transferability Via Decision Boundary Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing approaches often focus predominantly on attacking from a data-centric perspective, neglecting crucial aspects of the models. To address this issue, we propose a novel approach in this paper, coined Decision Boundary Adaptation (DBA).	Jiayu Zhang; Zhiyu Zhu; Zhibo Jin; Xinyi Wang; Huaming Chen; Kim-Kwang Raymond Choo;
53	Nearly Optimal Differentially Private ReLU Regression Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we investigate one of the most fundamental non-convex learning problems-ReLU regression-in the Differential Privacy (DP) model.	Meng Ding; Mingxi Lei; Shaowei Wang; Tianhang Zheng; Di Wang; Jinhui Xu;
54	Scaling Probabilistic Circuits Via Data Partitioning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: To remedy the situation, we show how PCs can be learned across multiple machines by recursively partitioning a distributed dataset, thereby unveiling a deep connection between PCs and federated learning (FL).	Jonas Seng; Florian Peter Busch; Pooja Prasad; Devendra Singh Dhami; Martin Mundt; Kristian Kersting;
55	Targeted Learning for Variable Importance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Current approaches largely rely on one-step procedures, which, while asymptotically efficient, can present higher sensitivity and instability in finite sample settings. To address these limitations, we propose a novel method by employing the targeted learning (TL) framework, designed to enhance robustness in inference for variable importance metrics.	Xiaohan Wang; Yunzhe Zhou; Giles Hooker;
56	Group-Agent Reinforcement Learning with Heterogeneous Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Under a more general heterogeneous setting where different agents learn using different algorithms, we advance GARL by designing novel and effective group-learning mechanisms.	Kaiyue Wu; Xiao-Jun Zeng; Tingting Mu;
57	Augmenting Online RL with Offline Data Is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a unified algorithm and analysis and show that augmenting confidence-based online RL algorithms with the offline dataset outperforms any pure online or offline algorithm alone and achieves state-of-the-art results under two learning metrics, i.e., sub-optimality gap and online learning regret.	Ruiquan Huang; Donghao Li; Chengshuai Shi; Cong Shen; Jing Yang;
58	Well-Defined Function-Space Variational Inference in Bayesian Neural Networks Via Regularized KL-Divergence Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This has motivated variational inference (VI) methods that pose priors directly on the function represented by the BNN rather than on weights. In this paper, we address a fundamental issue with such function-space VI approaches pointed out by Burt et al. (2020), who showed that the objective function (ELBO) is negative infinite for most priors of interest.	Tristan Cinquin; Robert Bamler;
59	Finding Interior Optimum of Black-box Constrained Objective with Bayesian Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, constrained Bayesian Optimization (CBO) often relies on heuristics, approximations, or relaxation of objectives, which can lead to weaker theoretical guarantees compared to canonical BO. In this paper, we address this gap by focusing on identifying the interior optimum of the constrained objective, deliberately excluding boundary candidates susceptible to noise perturbations.	Fengxue Zhang; Yuxin Chen;
60	A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Large language models (LLMs) have played a pivotal role in revolutionizing various facets of our daily existence. Solving attention regression is a fundamental task in optimizing …	Yeqi Gao; Zhao Song; Weixin Wang; Junze Yin;
61	Correlated Quantization for Faster Nonconvex Distributed Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We analyze the fore- front distributed non-convex optimization algorithm MARINA [Gorbunov et al., 2022] utilizing the proposed correlated quantizers and show that it outperforms the original MARINA and distributed SGD of Suresh et al. [2022] with regard to the communication complexity.	Andrei Panferov; Yury Demidovich; Ahmad Rammal; Peter Richt�rik;
62	Are You Doing Better Than Random Guessing? A Call for Using Negative Controls When Evaluating Causal Discovery Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this article, we propose to use negative controls as a common evaluation baseline by posing the question: Are we doing better than random guessing?	Anne Helby Petersen;
63	Improving Graph Contrastive Learning with Community Structure Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing GCL methods often suffer from two limitations: the repetitive message-passing mechanism in GNNs and the quadratic computational complexity of exhaustive node pair sampling in loss function. To address these issues, we propose an efficient and effective GCL framework that leverages community structure rather than relying on the intricate node-to-node adjacency information.	Xiang Chen; Kun Yue; Liang Duan; Lixing Yu;
64	Federated R�nyi Fair Inference in Federated Heterogeneous System Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The main challenge is estimating global fairness measures (e.g., Rényi or Pearson correlation) in an asynchronous, heterogeneous system. To address this, we propose the FedRényi algorithm, which regularizes fairness by Rényi correlation.	Zhiyong Ma; Yuanjie Shi; Yan Yan; Jian Chen;
65	Enhanced Equilibria-Solving Via Private Information Pre-Branch Structure in Adversarial Team Games Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods are fundamentally action-based, resulting in poor generalizability and low solving efficiency due to the exponential growth in the size of the transformed game. To address the above issues, we propose an efficient game transformation method based on private information, where all team members are represented by a single coordinator.	Chen Qiu; Haobo Fu; Kai Li; Jiajia Zhang; Xuan Wang;
66	FeDCM: Federated Learning of Deep Causal Generative Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we learn a proxy of the underlying structural causal model (SCM) with deep generative models from decentralized observational data sources possibly containing high-dimensional variables.	Md Musfiqur Rahman; Murat Kocaoglu;
67	I$^2$VAE: Interest Information Augmentation with Variational Regularizers for Cross-Domain Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, these approaches have limited information propagation, benefiting mainly overlapping users and those with rich interaction histories while neglecting non-overlapping (cold-start) and long-tailed users, who constitute the majority in real-world scenarios. To address this issue, we propose i$^2$VAE, a novel variational autoencoder (VAE)-based framework that enhances user interest learning with mutual information-based regularizers.	Xuying Ning; Wujiang Xu; Tianxin Wei; Xiaolei Liu;
68	What Is The Right Notion of Distance Between Predict-then-Optimize Tasks? Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose OTD$^3$ (Optimal Transport Decision-aware Dataset Distance), a novel dataset distance that incorporates downstream decisions in addition to features and labels.	Paula Rodriguez-Diaz; Lingkai Kong; Kai Wang; David Alvarez-Melis; Milind Tambe;
69	On Continuous Monitoring of Risk Violations Under Unknown Shift Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a general framework for the real-time monitoring of risk violations in evolving data streams.	Alexander Timans; Rajeev Verma; Eric Nalisnick; Christian A. Naesseth;
70	Adaptive Reward Design for Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: This is particularly problematic in environments with inherent uncertainty, where task completion may be unreliable despite progress on intermediate goals. To address this limitation, we propose a suite of reward functions that incentivize an RL agent to complete a task specified by an LTL formula as much as possible, and develop an adaptive reward shaping approach that dynamically updates reward functions during the learning process.	Minjae Kwon; Ingy ElSayed-Aly; Lu Feng;
71	Adaptive Threshold Sampling for Pure Exploration in Submodular Bandits Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we focus on the pure-exploration setting, where the goal is to identify a high-quality solution set using as few noisy queries as possible.	Wenjing Chen; Shuo Xing; Victoria G. Crawford;
72	Constraint-based Causal Discovery from A Collection of Conditioning Sets Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This becomes problematic in practice when variables with large support are present, as it makes CI tests less reliable due to near-deterministic relationships, thereby violating the faithfulness assumption. To address this issue, we propose a causal discovery algorithm that only uses CI tests where the conditioning sets are restricted to a given set of conditioning sets including the empty set $\mathcal{C}$.	Kenneth Lee; Bruno Ribeiro; Murat Kocaoglu;
73	Beyond Invisibility: Learning Robust Visible Watermarks for Stronger Copyright Protection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To establish long-term protection aiming at better robustness, we go beyond invisible perturbation, and propose a universal approach that embeds \textit{visible} watermarks that are \textit{hard-to-remove} into images.	Tianci Liu; Tong Yang; Quan Zhang; Qi Lei;
74	Best Arm Identification with Possibly Biased Offline Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We prove an impossibility result for adaptive algorithms without prior knowledge of the bias bound between online and offline distributions. To address this, we propose the LUCB-H algorithm, which introduces adaptive confidence bounds by incorporating an auxiliary bias correction to balance offline and online data within the LUCB framework.	Le Yang; Vincent Y. F. Tan; Wang Chi Cheung;
75	Trading Off Voting Axioms for Privacy Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we investigate tradeoffs among differential privacy (DP) and several important voting axioms: Pareto efficiency, SD-efficiency, PC-efficiency, Condorcet criterion, and Condorcet loser criterion.	Zhechen Li; Ao Liu; Lirong Xia; Yongzhi Cao; Hanpin Wang;
76	A Parallel Network for LRCT Segmentation and Uncertainty Mitigation with Fuzzy Sets Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Yet manual annotation is labor-intensive and prone to variability, while existing automated methods often fail to capture small airway branches in lower-resolution 3D data. To address this, we introduce \textbf{FuzzySR}, a parallel framework that merges super-resolution (SR) and segmentation.	Shiyi Wang; Yang Nan; Xiaodan Xing; Yingying Fang; Simon Lf Walsh; Guang Yang;
77	$s$-Maximal Ancestral Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we address that limitation.	Binghua Yao; Joris Marten Mooij;
78	FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose FedSPD, an efficient personalized federated learning algorithm for the decentralized setting, and show that it learns accurate models in low-connectivity networks.	I-Cheng Lin; Osman Yagan; Carlee Joe-Wong;
79	Contrast-CAT: Contrasting Activations for Enhanced Interpretability in Transformer-based Text Classifiers Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although activation-based attribution methods effectively explain transformer-based text classification models, our findings reveal that these methods can be undermined by class-irrelevant features within activations, leading to less reliable interpretations. To address this limitation, we propose Contrast-CAT, a novel activation contrast-based attribution method that refines token-level attributions by filtering out class-irrelevant features.	Sungmin Han; Jeonghyun Lee; Sangkyun Lee;
80	Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we develop a theory of moment alignment for DG.	Yuen Chen; Haozhe Si; Guojun Zhang; Han Zhao;
81	Online Generalized Magician�s Problem with Multiple Workers Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The system must decide on the acceptance of each task and its assignment to a worker, in order to maximize the accumulated reward within the budget. To address this problem, we propose the Online Worker Assignment (OWA) Algorithm.	Ruoyu Wu; Wei Bao; Ben Liang; Liming Ge;
82	Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient? Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, finding an exact solution to these maximization problems is often intractable and computationally expensive. Reflecting such realistic situations, in this paper, we delve into the effect of inexact maximizers of the acquisition functions.	Hwanwoo Kim; Chong Liu; Yuxin Chen;
83	Out-of-distribution Robust Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we consider the contextual robust optimization problem under an out-of-distribution setting.	Zhongze Cai; Hansheng Jiang; Xiaocheng Li;
84	Causal Eligibility Traces for Confounding Robust Off-Policy Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: A unifying theme in Artificial Intelligence is learning an effective policy to control an agent in an unknown environment in order to optimize a certain performance measure. Off-policy methods can significantly improve sample efficiency during training, since they allow an agent to learn from observed trajectories generated by different behavior policies, without directly deploying target policies in the underlying environment.	Junzhe Zhang; Elias Bareinboim;
85	Full Network Capacity Framework for Sample-Efficient Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose the Full Network Capacity (FNC) framework based on PR, which consists of two novel modules: Dormant Neuron Reactivation (DNR) and Stable Policy Update (SPU).	Wentao Yang; Xinyue Liu; Yunlong Gao; Wenxin Liang; Linlin Zong; Guanglu Wang; Xianchao Zhang;
86	Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents a novel and practical TSAD when the training data is contaminated with anomalies.	Thi Kieu Khanh Ho; Narges Armanfard;
87	Divide and Orthogonalize: Efficient Continual Learning with Local Model Space Projection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing CL methods often require either computationally expensive layer-wise gradient projections or large-scale storage of past task data, making them impractical for resource-constrained scenarios. To address these challenges, we propose a local model space projection (LMSP)-based continual learning framework that significantly reduces computational complexity from $\mathcal{O}(n^3)$ to $\mathcal{O}(n^2)$ while preserving both forward and backward knowledge transfer with minimal performance trade-offs.	Jin Shang; Simone Shao; Tian Tong; Fan Yang; Yetian Chen; Yang Jiao; Jia Liu; Yan Gao;
88	Truthful Elicitation of Imprecise Forecasts Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While proper scoring rules incentivize truthful reporting of precise forecasts, they fall short when forecasters face epistemic uncertainty about their beliefs, limiting their use in safety-critical domains where decision-makers (DMs) prioritize proper uncertainty management. To address this, we propose a framework for scoring \emph{imprecise forecasts}—forecasts given as a set of beliefs.	Anurag Singh; Siu Lun Chau; Krikamol Muandet;
89	Multi-Label Bayesian Active Learning with Inter-Label Relationships Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this paper, we propose a new multi-label active learning strategy to address both challenges.	Yuanyuan Qi; Jueqing Lu; Xiaohao Yang; Joanne Enticott; Lan Du;
90	A Unified Data Representation Learning for Non-parametric Two-sample Testing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose a representation-learning two-sample testing (RL-TST) framework.	Xunye Tian; Liuhua Peng; Zhijian Zhou; Mingming Gong; Arthur Gretton; Feng Liu;
91	On The Privacy Risks of Spiking Neural Networks: A Membership Inference Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we investigate the susceptibility of SNNs to Membership Inference Attacks (MIAs)-a major privacy threat where an adversary attempts to determine whether a given sample was part of the training dataset.	Junyi Guan; Abhijith Sharma; Chong Tian; Salem Lahlou;
92	FlightPatchNet: Multi-Scale Patch Network with Differential Coding for Short-Term Flight Trajectory Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The second issue is that real-world flight trajectories involve underlying temporal dependencies, and most existing methods fail to reveal the hidden complex temporal variations and extract features from one single time scale. To address the above issues, we propose FlightPatchNet, a multi-scale patch network with differential coding for flight trajectory prediction.	Lan Wu; Xuebin Wang; Ruijuan Chu; Guangyi Liu; Jing Zhang; Linyu Wang;
93	Dependent Randomized Rounding for Budget Constrained Experimental Design Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a framework that applies a dependent randomized rounding procedure to convert assignment probabilities into binary treatment decisions.	Khurram Yamin; Edward Kennedy; Bryan Wilder;
94	Budget Allocation Exploiting Label Correlation Between Instances Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this study, we introduce an innovative budget allocation method for graph instance annotation in crowdsourcing environments, where both the labels of instances and their correlations are unknown and need to be estimated simultaneously.	Adithya Kulkarni; Mohna Chakraborty; Sihong Xie; Qi Li;
95	Coevolutionary Emergent Systems Optimization with Applications to Ultra-High-Dimensional Metasurface Design : OAM Wave Manipulation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Traditional optimization algorithms face significant challenges in maintaining both accuracy and computational efficiency when dealing with such ultra-high-dimensional problems. This paper presents a novel Coevolutionary Emergent Systems Optimization (CESO) algorithm that integrates coevolutionary dynamics, emergent behavior, and adaptive mechanisms to address these challenges.	Zhengxuan Jiang; Guowen Ding; Wen Jiang;
96	Informative Synthetic Data Generation for Thorax Disease Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose \emph{Informative Data Selection} (IDS), a principled sample re-weighting framework grounded in the Information Bottleneck (IB) principle.	Yancheng Wang; Rajeev Goel; Marko Jojic; Alvin C. Silva; Teresa Wu; Yingzhen Yang;
97	Asymptotically Optimal Linear Best Feasible Arm Identification with Fixed Budget Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, a notable gap remains in the literature: the exact exponential rate at which the error probability approaches zero has yet to be established, even in the relatively simple setting of $K$-armed bandits with Gaussian noise. In this paper, we address this gap by examining the problem within the context of linear bandits.	Jie Bian; Vincent Y. F. Tan;
98	Optimal Transport Alignment of User Preferences from Ratings and Texts Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While aligning preference factors from ratings and texts, as a solution, shows improvements, existing methods impose restrictive one-to-one factor correspondences and underutilize cross-modal interest signals. We propose an optimal transport (OT) approach to address these gaps.	Nhu-Thuat Tran; Hady W. Lauw;
99	Learning Algorithms for Multiple Instance Regression Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work we show that it is indeed possible to efficiently learn linear regressors in MIR when given access to random bags of uniformly randomly sampled primary instance chosen as the bag-label in which the feature vectors are independently sampled from Gaussian distributions.	Aaryan Gupta; Rishi Saket;
100	Approximate Bayesian Inference Via Bitstring Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We consider both 2D densities and quantized neural networks, where we introduce a tractable learning approach using probabilistic circuits.	Aleksanteri Sladek; Martin Trapp; Arno Solin;
101	The Consistency Hypothesis in Uncertainty Quantification for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we examine the implicit assumption behind several UQ methods, which use generation consistency as a proxy for confidence-an idea we formalize as the consistency hypothesis.	Quan Xiao; Debarun Bhattacharjya; Balaji Ganesan; Radu Marinescu; Katya Mirylenka; Nhan H Pham; Michael Glass; Junkyu Lee;
102	Sparse Structure Exploration and Re-optimization for Vision Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this study, we introduce a new framework, Sparse Structure Exploration and Re-optimization (SERo), specifically designed to maximize pruning efficiency in ViTs.	Sangho An; Jinwoo Kim; Keonho Lee; Jingang Huh; Chanwoong Kwak; Yujin Lee; Moonsub Jin; Jangho Kim;
103	MSCGrapher: Learning Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, in real-world scenarios, persistent and significant inter-series correlations are challenging to be represented in a static way and the strength of correlations varies across different time scales. In this paper, we address this challenge by modeling the complex inter-series relationships through dynamical correlations, considering the varying strengths of correlations.	Xian Yang; Zhenguo Zhang; Shihao Lu;
104	Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this paper, we propose GroVE, a post-hoc approach to obtaining probabilistic embeddings from frozen VLMs.	Aishwarya Venkataramanan; Paul Bodesheim; Joachim Denzler;
105	Stein Variational Evolution Strategies Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To improve gradient-free sampling and optimization, we propose Stein Variational CMA-ES, a novel gradient-free SVGD-like method that combines the efficiency of evolution strategies with SVGD-based repulsion forces.	Cornelius V. Braun; Robert Tjarko Lange; Marc Toussaint;
106	Adapting Prediction Sets to Distribution Shifts Without Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Through extensive experiments on a number of large-scale datasets and neural network architectures, we show that our methods provide consistent improvement over existing baselines and nearly match the performance of fully supervised methods.	Kevin Kasa; Zhiyu Zhang; Heng Yang; Graham W. Taylor;
107	Towards Provably Efficient Learning of Imperfect Information Extensive-Form Games with Linear Function Approximation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Based on this, we further propose a "least-squares loss estimator", which we call the fictitious least-squares loss estimator. Through integrating this estimator with the follow-the-regularized-leader (FTRL) framework, we propose the fictitious least-squares follow-the-regularized-leader ($\text{F}^2\text{TRL}$) algorithm, which achieves a provable $\widetilde{\mathcal{O}}(\lambda\sqrt{d H^2 T})$ regret guarantee in the large $T$ regime, where $d$ is the ambient dimension of the feature mapping, $H$ is the horizon length, $\lambda$ is a "balance coefficient" and $T$ is the number of episodes.	Canzhe Zhao; Shuze Chen; Weiming Liu; Haobo Fu; Qiang Fu; Shuai Li;
108	STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, MOO algorithm design remains in its infancy and many existing MOO methods suffer from unsatisfactory convergence rate and sample complexity performance. To address this challenge, in this paper, we propose an algorithm called STIMULUS (stochastic path-integrated multi-gradient recursive estimator), a new and robust approach for solving MOO problems.	Zhuqing Liu; Chaosheng Dong; Michinari Momma; Simone Shao; Shaoyuan Xu; Yan Gao; Haibo Yang; Jia Liu;
109	FALCON: Adaptive Cross-Domain APT Attack Investigation with Federated Causal Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a novel approach to APT attack investigation, FALCON, which captures complex causal relationships between entities from discrete audit logs and constructs cross-domain provenance graphs, enabling rapid and accurate identification of potential APT activities.	Jialu Tang; Yali Gao; Xiaoyong Li; Jiawei Li; Shui Yu; Binxing Fang;
110	Partial-Label Learning with Conformal Candidate Cleaning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: Partial-label learning (PLL) aims at training a classifier in this challenging setting, where each instance is associated with a set of candidate labels and one correct, but unknown, class label. A multitude of algorithms targeting this setting exists and, to enhance their prediction quality, several extensions that are applicable across a wide range of PLL methods have been introduced.	Tobias Fuchs; Florian Kalinke;
111	Distributionally and Adversarially Robust Logistic Regression Via Intersecting Wasserstein Balls Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We study the underlying optimization problem, develop efficient solution algorithms, and demonstrate that the proposed method outperforms benchmark approaches on standard datasets.	Aras Selvi; Eleonora Kreacic; Mohsen Ghassemi; Vamsi K. Potluru; Tucker Balch; Manuela Veloso;
112	The Relativity of Causal Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Next, using sheaf theory, we construct the network sheaf and cosheaf of causal knowledge. These structures enable the transfer of causal knowledge across the network while incorporating interventional consistency and the perspective of the subjects, ultimately leading to the formal, mathematical definition of relative causal knowledge.	Gabriele D�Acunto; Claudio Battiloro;
113	Variational Learning of Gaussian Process Latent Variable Models Through Stochastic Gradient Annealed Importance Sampling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose VAIS-GPLVM, a variational Annealed Importance Sampling method that leverages time-inhomogeneous unadjusted Langevin dynamics to construct the variational posterior.	Jian Xu; Shian Du; Junmei Yang; Qianli Ma; Delu Zeng; John Paisley;
114	Toward Universal Laws of Outlier Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: When a variety of anomalous features motivate flagging different samples as outliers, Algorithmic Information Theory (AIT) offers a principled way to unify them in terms of a sample’s randomness deficiency.	Aram Ebtekar; Yuhao Wang; Dominik Janzing;
115	A Trajectory-Based Bayesian Approach to Multi-Objective Hyperparameter Optimization with Epoch-Aware Trade-Offs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Despite the limited research in this area, existing methods commonly identify the trade-offs only at the end of model training, overlooking the fact that trade-offs can emerge at earlier epochs in cases such as overfitting. To bridge this gap, we propose an enhanced multi-objective hyperparameter optimization problem that treats the number of training epochs as a decision variable, rather than merely an auxiliary parameter, to account for trade-offs at an earlier training stage.	Wenyu Wang; Zheyi Fan; Szu Hui Ng;
116	Limit-sure Reachability for Small Memory Policies in POMDPs Is NP-complete Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we study the limit-sure reachability problem for POMDPs with a fixed amount of memory.	Ali Asadi; Krishnendu Chatterjee; Raimundo Saona; Ali Shafiee;
117	ODD: Overlap-aware Estimation of Model Performance Under Distribution Shift Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: With an intuitive assumption that the target disagreement should be no more than the source disagreement in the overlapping region due to high enough support, we devise Overlap-aware Disagreement Discrepancy (\odd).	Aayush Mishra; Anqi Liu;
118	Statistical Significance of Feature Importance Rankings Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: Leveraging novel ideas from hypothesis testing, we devise techniques that ensure the most important features are correct with high-probability guarantees.	Jeremy Goldwasser; Giles Hooker;
119	Adversarial Training May Induce Deteriorating Distributions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper reveals a surprising behavior in AT, namely that the distribution induced by adversarial perturbations during AT becomes progressively more difficult to learn.	Runzhi Tian; Yongyi Mao;
120	Optimal Transport for Probabilistic Circuits Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a novel optimal transport framework for probabilistic circuits (PCs).	Adrian Ciotinga; YooJung Choi;
121	RDI: An Adversarial Robustness Evaluation Metric for Deep Neural Networks Based on Model Statistical Features Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address the aforementioned issues, we propose a novel adversarial robustness evaluation metric, Robustness Difference Index (RDI), which is based on model statistical features.	Jialei Song; Xingquan Zuo; Feiyang Wang; Hai Huang; Tianle Zhang;
122	Expert-In-The-Loop Causal Discovery: Iterative Model Refinement Using Expert Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Here we propose a hybrid, iterative structure learning approach that combines domain knowledge with data-driven insights to assist researchers in constructing DAGs.	Ankur Ankan; Johannes Textor;
123	EERO: Early Exit with Reject Option for Efficient Classification with Limited Budget Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose EERO, a new methodology to translate the problem of early exiting to a problem of using multiple classifiers with reject option in order to better select the exiting head for each instance.	Florian Valade; Mohamed Hebiri; Paul Gay;
124	FDR-SVM: A Federated Distributionally Robust Support Vector Machine Via A Mixture of Wasserstein Balls Ambiguity Set Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We study a federated classification problem over a network of multiple clients and a central server, in which each client’s local data remains private and is subject to uncertainty in both the features and labels.	Michael Ibrahim; Heraldo Rozas; Nagi Gebraeel; Weijun Xie;
125	Root Cause Analysis of Failures from Partial Causal Structures Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Beyond the single root cause scenario, we propose a practical extension for settings with multiple root causes and partial causal knowledge.	Azam Ikram; Kenneth Lee; Shubham Agarwal; Shiv Kumar Saini; Saurabh Bagchi; Murat Kocaoglu;
126	Experimentation Under Treatment Dependent Network Interference Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This creates a novel and unexplored problem: estimating treatment effects when the interference network is determined by treatment allocation. In this work, we address this gap by proposing two single-experiment estimators for scenarios where network edges depend on nodal treatments constructed from instrumental variables derived from neighbourhood treatments.	Shiv Shankar; Ritwik Sinha; Madalina Fiterau;
127	Symbiotic Local Search for Small Decision Tree Policies in MDPs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper contributes a local search approach to find policies with good value, represented by small decision trees.	Roman Andriushchenko; Milan Ceska; Debraj Chakraborty; Sebastian Junges; Jan Kretinsky; Filip Mac�k;
128	Moments of Causal Effects Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We conduct experiments to illustrate the estimation of the moments of causal effects from finite samples and demonstrate their practical application using a real-world medical dataset.	Yuta Kawakami; Jin Tian;
129	Decomposition of Probabilities of Causation with Two Mediators Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this study, we investigate the path-specific probability of necessity and sufficiency (PNS) to decompose the total PNS into path-specific components along distinct causal pathways between treatment and outcome, incorporating two mediators.	Yuta Kawakami; Jin Tian;
130	Beyond Sin-Squared Error: Linear Time Entrywise Uncertainty Quantification for Streaming PCA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a novel statistical inference framework for streaming principal component analysis (PCA) using Oja’s algorithm, enabling the construction of confidence intervals for individual entries of the estimated eigenvector.	Syamantak Kumar; Shourya Pandey; Purnamrita Sarkar;
131	Generalised Probabilistic Modelling and Improved Uncertainty Estimation in Comparative LLM-as-a-judge Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper explores generalised probabilistic modelling and uncertainty estimation in comparative LLM-as-a-judge frameworks.	Yassir Fathullah; Mark Gales;
132	BELIEF – Bayesian Sign Entropy Regularization for LIME Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a Bayesian Regularization approach to reduce sign flips, which in turn stabilizes feature rankings and ensures significantly higher consistency in explanations.	Revoti Prasad Bora; Philipp Terh�rst; Raymond Veldhuis; Raghavendra Ramachandra; Kiran Raja;
133	The Causal Information Bottleneck and Optimal Causal Variable Abstractions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: We propose the Causal Information Bottleneck (CIB), a causal extension of the IB, which compresses a set of chosen variables while maintaining causal control over a target variable.	Francisco N. F. Q. Simoes; Mehdi Dastani; Thijs van Ommen;
134	Offline Changepoint Detection With Gaussian Processes Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This work proposes Segmenting changepoint Gaussian process regression (SegCPGP), an offline changepoint detection method that integrates Gaussian process regression with the changepoint kernel, the likelihood ratio test and binary search.	Janneke Verbeek; Tom Heskes; Yuliya Shapovalova;
135	Enhancing Uncertainty Quantification in Large Language Models Through Semantic Graph Density Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While several methods have been proposed for UQ in LLMs, they suffer from key limitations, such as overlooking fine-grained semantic relationships among answers and neglecting answer probabilities. To address these issues, we propose Semantic Graph Density (SGD).	Zhaoye Li; Siyuan Shen; Wenjing Yang; Ruochun Jin; Huan Chen; Ligong Cao; Jing Ren;
136	Using Submodular Optimization to Approximate Minimum-Size Abductive Path Explanations for Tree-Based Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we focus on finding minimal sets of features along the paths leading to the decision, called path-abductive explanations.	Louenas Bounia;
137	Explaining Negative Classifications of AI Models in Tumor Diagnosis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present a definition of and algorithm for providing explanations of absence; that is, explanations of negative classifications in the context of healthcare AI.	David A. Kelly; Hana Chockler; Nathan Blake;
138	Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We investigate the estimation of the causal effect of a treatment variable on an outcome in the presence of a latent confounder.	Yaroslav Kivva; Sina Akbari; Saber Salehkaleybar; Negar Kiyavash;
139	Selective Blocking for Message-Passing Neural Networks on Heterophilic Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our key insight is to decide not to propagate along uncertain edges adaptively.	Yoonhyuk Choi; Taewook Ko; Jiho Choi; Chong-Kwon Kim;
140	Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this work, we propose a model-based CTRL algorithm that achieves both sample and computational efficiency.	Runze Zhao; Yue Yu; Adams Yiyue Zhu; Chen Yang; Dongruo Zhou;
141	Efficient Algorithms for Logistic Contextual Slate Bandits with Bandit Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our objective is to develop algorithms that maximize cumulative reward over $T$ rounds while maintaining low per-round computational costs. We propose two algorithms, Slate-GLM-OFU and Slate-GLM-TS, that accomplish this goal.	Tanmay Goyal; Gaurav Sinha;
142	A Multivariate Unimodality Test Harnessing The Dip Statistic of Mahalanobis Distances Over Random Projections Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: By extrapolating one-dimensional unimodality principles to multi-dimensional spaces through linear random projections and leveraging point-to-point distancing, our method, rooted in $\alpha$-unimodality assumptions, presents a novel multivariate unimodality test named $\textit{mud-pod}$.	Prodromos Kolyvakis; Aristidis Likas;
143	Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference Over Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Indeed, despite representing probability distributions, intractable DGMs deny probabilistic foundations by their inability to answer even the most basic inference queries without approximations or design choices specific to a very narrow range of queries. To address this limitation, we propose probabilistic graph circuits (PGCs), a framework of tractable DGMs that provide exact and efficient probabilistic inference over (arbitrary parts of) graphs.	Milan Papez; Martin Rektoris; Vaclav Smidl; Tom� Pevn�;
144	Adaptive Human-Robot Collaboration Using Type-Based IRL Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, human performance may degrade due to various factors (e.g., fatigue, trust) which can manifest unpredictably, and typically results in diminished output and reduced quality. To address this challenge toward successful HRCs, we present a human-aware approach to collaboration using a novel multi-agent decision-making framework.	Prasanth Sengadu Suresh; Prashant Doshi; Bikramjit Banerjee;
145	Privacy-Preserving Neural Processes for Probabilistic User Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes a novel framework for privacy-preserving probabilistic user modeling that integrates uncertainty quantification and differential privacy (DP).	Amir Sonee; Haripriya Harikumar; Alex H�m�l�inen; Lukas Prediger; Samuel Kaski;
146	InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: We introduce InfoDPCCA, a dynamic probabilistic Canonical Correlation Analysis (CCA) framework designed to model two interdependent sequences of observations.	Shiqin Tang; Shujian Yu;
147	Tuning-Free Coreset Markov Chain Monte Carlo Via Hot DoG Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a learning-rate-free stochastic gradient optimization procedure, Hot-start Distance over Gradient (Hot DoG), for training coreset weights in Coreset MCMC without user tuning effort.	Naitong Chen; Jonathan H. Huggins; Trevor Campbell;
148	Multi-group Uncertainty Quantification for Long-form Text Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In our work, given some long-form text generated by an LLM, we study uncertainty at both the level of individual claims contained within the output (via calibration) and across the entire output itself (via conformal prediction).	Terrance Liu; Steven Wu;
149	Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we show that physics-informed neural networks (PINNs) can be trained to approximate the solution PDF.	Chun-Wei Kong; Luca Laurenti; Jay McMahon; Morteza Lahijanian;
150	Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing data reconstruction attacks have important limitations: they often rely on assumptions about the clients’ data distribution or their efficiency significantly degrades when batch sizes exceed just a few tens of samples. In this work, we introduce a novel data reconstruction attack that overcomes these limitations.	Francesco Diana; Andr� Nusser; Chuan Xu; Giovanni Neglia;
151	Simulation-based Inference for High-dimensional Data Using Surjective Sequential Neural Likelihood Estimation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: We present Surjective Sequential Neural Likelihood (SSNL) estimation, a novel member in the family of methods for simulation-based inference (SBI).	Simon Dirmeier; Carlo Albert; Fernando Perez-Cruz;
152	Mixup Regularization: A Probabilistic Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This work introduces a novel framework for mixup regularization based on probabilistic fusion that is better suited for conditional density estimation tasks.	Yousef El-Laham; Niccolo Dalmasso; Svitlana Vyetrenko; Vamsi K. Potluru; Manuela Veloso;
153	Over The Top-1: Uncertainty-Aware Cross-Modal Retrieval with CLIP Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a training-free framework for uncertainty estimation in cross-modal retrieval.	Lluis Gomez;
154	LoSAM: Local Search in Additive Noise Models with Mixed Mechanisms and General Noise for Global Causal Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose local search in additive noise models, LoSAM, a topological ordering method for learning a unique DAG in ANMs with mixed causal mechanisms and general noise distributions.	Sujai Hiremath; Promit Ghosal; Kyra Gan;
155	Learning to Sample in Stochastic Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We consider a PAC-Bayes analysis of stochastic optimization algorithms, and devise a new SGDA algorithm inspired from our bounds.	Sijia Zhou; Yunwen Lei; Ata Kaban;
156	Scalable Bayesian Low-Rank Adaptation of Large Language Models Via Stochastic Variational Subspace Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work we present $\textbf{Scala}$ble $\textbf{B}$ayesian $\textbf{L}$ow-Rank Adaptation via Stochastic Variational Subspace Inference (ScalaBL).	Colin Samplawski; Adam D. Cobb; Manoj Acharya; Ramneet Kaur; Susmit Jha;
157	Geodesic Slice Sampler for Multimodal Distributions with Strong Curvature Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a method that generalizes Hit-and-Run slice sampling to more general geometries tailored to the target distribution, by approximating geodesics as solutions to differential equations.	Bernardo Williams; Hanlin Yu; Hoang Phuc Hau Luu; Georgios Arvanitidis; Arto Klami;
158	Aggregating Data for Optimal Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In the case of MIR, the bag-label is the label of an undisclosed instance from the bag, while in LLP, the bag-label is the mean of the bag’s labels. In this paper, we study for various loss functions in MIR and LLP, what is the optimal way to partition the dataset into bags such that the utility for downstream tasks like linear regression is maximized.	Sushant Agarwal; Yukti Makhija; Rishi Saket; Aravindan Raghuveer;
159	Computationally Efficient Methods for Invariant Feature Selection with Sparsity Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Prior methods are limited to combinatorially searching over the space of all sparse models, but we present a different loss function.	Jane Du; Arindam Banerjee;
160	Lower Bound on Howard Policy Iteration for Deterministic Markov Decision Processes Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although Howard’s algorithm performs well in practice, as experimental studies suggested, the best known upper bound is exponential and the current known lower bound is as follows: For the input size $I$, the algorithm requires $\widetilde{\Omega}(\sqrt{I})$ iterations, where $\widetilde{\Omega}$ hides the poly-logarithmic factors, i.e., the current lower bound on iterations is sub-linear with respect to the input size. Our main result is an improved lower bound for this fundamental algorithm where we show that for the input size $I$, the algorithm requires $\widetilde{\Omega}(I)$ iterations.	Ali Asadi; Krishnendu Chatterjee; Jakob de Raaij;
161	Learning Multi-interest Embedding with Dynamic Graph Cluster for Sequention Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we focus on how to fully capture the changing relations when capturing the user multi-interest representations.	Xiao Chunjing; Ranhao Guo; Zhang Yongwang; Xiaoming Wu;
162	A Mirror Descent Perspective of Smoothed Sign Descent Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce the dual dynamics of smoothed sign descent with stability constant $\varepsilon$ for regression problems, formulated using the mirror descent framework.	Shuyang Wang; Diego Klabjan;
163	Nonparametric Bayesian Multi-Facet Clustering for Longitudinal Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we explore Bayesian multi-facet clustering modelling for temporal data using nonparametric priors to select an appropriate number of clusters automatically and using variational inference to efficiently explore the parameter space.	Luwei Wang; Kieran Richards; Sohan Seth;
164	Label Distribution Learning Using The Squared Neural Family on The Probability Simplex Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a novel label distribution learning model SNEFY-LDL, which estimates a probability distribution of all possible label distributions over the simplex, by unleashing the expressive power of the recently introduced Squared Neural Family (SNEFY), a new class of tractable probability models.	Daokun Zhang; Russell Tsuchida; Dino Sejdinovic;
165	Flat Posterior Does Matter For Bayesian Model Averaging Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this work, we explore how posterior flatness influences BMA generalization and empirically demonstrate that \emph{(1) most approximate Bayesian inference methods fail to yield a flat posterior} and \emph{(2) BMA predictions, without considering posterior flatness, are less effective at improving generalization}.	Sungjun Lim; Jeyoon Yeom; Sooyon Kim; Hoyoon Byun; Jinho Kang; Yohan Jung; Jiyoung Jung; Kyungwoo Song;
166	Probabilistic Explanations for Regression Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The goal of this paper is to extend the concept of probabilistic explanations to the regression setting, treating the target regressor as a black box function.	Fr�d�ric Koriche; Jean-Marie Lagniez; Chi Tran;
167	Multi-armed Bandits with Missing Outcomes Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Despite the practical relevance of this challenge, no rigorous methodology currently exists for systematically handling missingness, especially when the missingness mechanism is not random. In this paper, we address this gap in the context of multi-armed bandits (MAB) with missing outcomes by analyzing the impact of different missingness mechanisms on achievable regret bounds.	Ilia Mahrooghi; Mahshad Moradi; Sina Akbari; Negar Kiyavash;
168	SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce SpinSVAR, a novel method for estimating a (linear) structural vector autoregression (SVAR) from time-series data under a sparse input assumption.	Panagiotis Misiakos; Markus P�schel;
169	Optimal Submanifold Structure in Log-linear Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In the modeling of discrete distributions using log-linear models, the model selection process is equivalent to imposing zero-value constraints on a subset of natural parameters, which is an established concept in information geometry.	Zhou Derun; Mahito Sugiyama;
170	HDP-Flow: Generalizable Bayesian Nonparametric Model for Time Series State Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce HDP-Flow, a Bayesian nonparametric (BNP) model for unsupervised state discovery in dynamic, non-stationary time series data.	Sana Tonekaboni; Tina Behrouzi; Addison Weatherhead; Emily Fox; David Blei; Anna Goldenberg;
171	Optimal Zero-shot Regret Minimization for Selective Classification with Out-of-Distribution Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: On the other hand, making an informed choice is impossible without samples from both in- and out-distribution. We propose an optimal zero-shot black-box method for SCOD that aggregates off-the-shelf detectors, is based on the principle of regret minimization, and therefore provides guarantees on the worst-case performance.	Eduardo Dadalto C�mara Gomes; Marco Romanelli;
172	Black-box Optimization with Unknown Constraints Via Overparameterized Deep Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To model both the objective function and constraints, we use Deep Neural Networks (DNNs) instead of Gaussian Processes (GPs) to improve scalability and handle complex structured data.	Dat Phan Trong; Hung The Tran; Sunil Gupta;
173	Bayesian Optimization Over Bounded Domains with The Beta Product Kernel Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address the limitation, we introduce the Beta kernel, a non-stationary kernel induced by a product of Beta distribution density functions.	Huy Hoang Nguyen; Han Zhou; Matthew B. Blaschko; Aleksei Tiulpin;
174	Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: As a result, learning cyclic causal structures remains a challenging problem. In this paper, we offer new insights on this problem in the context of linear non-Gaussian models.	Mathias Drton; Marina Garrote-L�pez; Niko Nikov; Elina Robeva; Y. Samuel Wang;
175	Nonlinear Causal Discovery for Grouped Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce effective and novel solutions for both steps in the vector case, demonstrating strong performance in simulations.	Konstantin G�bler; Tobias Windisch; Mathias Drton;
176	Causal Inference Amid Missingness-Specific Independences and Mechanism Shifts Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Yet, in many real-world settings, missing data can alter decision-making processes, as the absence of key information may affect downstream actions and states. To overcome this limitation, we introduce $lm$-SCMs and $lm$-graphs, which extend $m$-graphs by integrating a label set that represents relevant context-specific independencies (CSI), accounting for mechanism shifts induced by missingness.	Johan de Aguas; Leonard Henckel; Johan Pensar; Guido Biele;
177	Valid Bootstraps for Network Embeddings with Applications to Network Visualisation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: We utilise an exchangeable network test that can empirically validate bootstrap samples generated by any method. Existing methods fail this test, so we propose a principled, distribution-free network bootstrap using k-nearest neighbour smoothing, that can pass this exchangeable network test in many synthetic and real-data scenarios.	Emerald Dilworth; Ed Davis; Daniel John Lawson;
178	Guaranteed Prediction Sets for Functional Surrogate Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a method for obtaining statistically guaranteed prediction sets for functional machine learning methods: surrogate models which map between function spaces, motivated by the need to build reliable PDE emulators.	Ander Gray; Vignesh Gopakumar; Sylvain Rousseau; Sebastien Destercke;
179	Temperature Optimization for Bayesian Deep Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this work, we propose a data-driven approach to select the temperature that maximizes test log-predictive density, treating the temperature as a model parameter and estimating it directly from the data.	Kenyon Ng; Chris van der Heide; Liam Hodgkinson; Susan Wei;
180	Learning Robust XGBoost Ensembles for Regression Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a novel method for training robust tree-based boosted ensembles applicable to any task that employs a differentiable loss function, leveraging the XGBoost framework.	Atri Vivek Sharma; Panagiotis Kouvaros; Alessio Lomuscio;
181	MSP-SR: Multi-Stage Probabilistic Generative Super Resolution with Scarce High-Resolution Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose Multi-Stage Probabilistic Super Resolution (MSP-SR), a cascaded few-shot learning framework for super-resolution through multi-stage transfer learning.	Ruike Zhu; Matthew Charles Weston; Hanwen Zhang; Arindam Banerjee;
182	Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Building on this foundation, we address a key challenge in federated graph learning: missing neighbor information, which inflates CP set sizes and reduces efficiency. To mitigate this, we propose a variational autoencoder (VAE)-based architecture that reconstructs missing neighbors while preserving data privacy.	�mer Faruk Akg�l; Rajgopal Kannan; Viktor Prasanna;
183	CATE Estimation With Potential Outcome Imputation From Local Regression Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: One of the most significant challenges in Conditional Average Treatment Effect (CATE) estimation is the statistical discrepancy between distinct treatment groups. To address this issue, we propose a model-agnostic data augmentation method for CATE estimation.	Ahmed Aloui; Juncheng Dong; Cat Phuoc Le; Vahid Tarokh;
184	Conditional Average Treatment Effect Estimation Under Hidden Confounders Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a CATE estimation method based on a pseudo-confounder generator and a CATE model that aligns the learned potential outcomes from the observational data with those observed from the RCT.	Ahmed Aloui; Juncheng Dong; Ali Hasan; Vahid Tarokh;
185	MOHITO: Multi-Agent Reinforcement Learning Using Hypergraphs for Task-Open Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a novel category of RL for addressing task openness, modeled using a task-open Markov game.	Gayathri Anil; Prashant Doshi; Daniel Alan Redder; Adam Eck; Leen-Kiat Soh;
186	Evasion Attacks Against Bayesian Predictive Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: However, most of the research in adversarial machine learning has focused on studying weaknesses against evasion or poisoning attacks to predictive models in classical setups, with the susceptibility of Bayesian predictive models to attacks remaining underexplored. This paper introduces a general methodology for designing optimal evasion attacks against such models.	Pablo G. Arce; Roi Naveiro; David R�os Insua;
187	Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this paper, we combine MCTMs with state-of-the-art and autoregressive NFs to leverage the transparency of MCTMs for modeling interpretable feature effects on the marginal distributions in the first step and the flexibility of neural-network-based NFs techniques to account for complex and non-linear relationships in the joint data distribution.	Marcel Arpogaus; Thomas Kneib; Thomas Nagler; David R�gamer;
188	Revisiting The Berkeley Admissions Data: Statistical Tests for Causal Hypotheses Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In the process, we introduce a statistical test for causal hypothesis testing based on Pearl’s instrumental-variable inequalities \citep{Pearl95}.	Sourbh Bhadane; Joris Marten Mooij; Philip Boeken; Onno Zoeter;
189	Multi-Cost-Bounded Reachability Analysis of POMDPs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a reduction of the multi-cost-bounded problem to unbounded reachability probabilities on an unfolding of the original POMDP.	Alexander Bork; Joost-Pieter Katoen; Tim Quatmann; Svenja Stein;
190	Causal Models for Growing Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Real-world networks grow over time; statistical models based on node exchangeability are not appropriate. Instead of constraining the structure of the distribution of edges, we propose that the relevant symmetries refer to the causal structure between them.	Gecia Bravo-Hermsdorff; Kayvan Sadeghi; Lee M. Gunderson;
191	Fast Non-convex Matrix Sensing with Optimal Sample Complexity Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We study the problem of recovering an unknown $d_1 \times d_2$ rank-$r$ matrix from $m$ random linear measurements.	Jian-Feng Cai; Tong Wu; Ruizhe Xia;
192	Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present attributed unfolded adjacency spectral embedding (AUASE), a stable unsupervised representation learning framework for dynamic networks in which nodes are attributed with time-varying covariate information.	Emma Ceccherini; Ian Gallagher; Andrew Jones; Daniel John Lawson;
193	Building Conformal Prediction Intervals with Approximate Message Passing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: However, its evaluation may be computationally costly, especially in the high-dimensional setting where the dimensionality and sample sizes are both large and of comparable magnitudes. To address this challenge in the context of generalized linear regression, we propose a novel algorithm based on Approximate Message Passing (AMP) to accelerate the computation of prediction intervals using full conformal prediction, by approximating the computation of conformity scores.	Lucas Clart�; Lenka Zdeborov�;
194	Measuring IIA Violations in Similarity Choices with Bayesian Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose two statistical methods to test for IIA: a classical goodness-of-fit test and a Bayesian counterpart based on the framework of Posterior Predictive Checks (PPC).	Hugo Sales Correa; Suryanarayana Sankagiri; Daniel R. Figueiredo; Matthias Grossglauser;
195	Learning Causal Response Representations Through Direct Effect Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a novel approach for learning causal response representations.	Homer Durand; Gherardo Varando; Gustau Camps-Valls;
196	Proximal Interacting Particle Langevin Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: Leveraging proximal Markov chain Monte Carlo techniques and interacting particle Langevin algorithms, we propose three algorithms tailored to the problem of estimating parameters in a non-differentiable statistical model.	Paula Cordero Encinar; Francesca Romana Crucinio; Omer Deniz Akyildiz;
197	Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this paper, we use a two-step procedure to train Bayesian Neural Networks that provide uncertainties over the solutions to differential equation systems provided by PINNs.	Pablo Flores; Olga Graf; Pavlos Protopapas; Karim Pichara;
198	Conformal Prediction Without Nonconformity Scores Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This suggests an interesting connection between CP and preference learning, in particular learning-to-rank methods, and makes CP amenable to training data in the form of (qualitative) preferences. Elaborating on this connection, we propose methods for preference-based CP and show their usefulness in real-world classification tasks.	Jonas Hanselle; Alireza Javanmardi; Tobias Florin Oberkofler; Yusuf Sale; Eyke H�llermeier;
199	Quantum Speedups for Bayesian Network Structure Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We answer the question in the affirmative by giving two algorithms achieving $c \leq 1.817$ and $c \leq 1.982$ assuming the number of potential parent sets is, respectively, subexponential and $O(1.453^n)$.	Juha Harviainen; Kseniya Rychkova; Mikko Koivisto;
200	RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods often struggle to maintain strong worst-group accuracy, particularly at high pruning rates, across balanced and imbalanced datasets. To address this challenge, we propose RCAP, a Robust, Class-Aware, Probabilistic dynamic dataset pruning algorithm for classification tasks.	Atif Hassan; Swanand Khare; Jiaul H. Paik;
201	SPvR: Structured Pruning Via Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Deep neural networks have achieved state-of-the-art performance in multiple domains but are increasingly resource-intensive, limiting their deployment on constrained devices. We introduce Structured Pruning via Ranking (SPvR), a novel structured pruning approach to address this challenge for classification tasks.	Atif Hassan; Jiaul H. Paik; Swanand Khare;
202	Lower Bounds on The Size of Markov Equivalence Classes Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Under the assumptions of acyclicity, causal sufficiency, and a uniform model prior, Markov equivalence classes are known to be small on average. In this paper, we show that this is no longer the case when any of these assumptions is relaxed.	Erik L Jahn; Frederick Eberhardt; Leonard Schulman;
203	Fast Calculation of Feature Contributions in Boosting Trees Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although coefficients of determination ($R^2$) allow for comparative assessment of individual features, individualizing $R^2$ is challenged by the underlying quadratic losses. To address this, we propose Q-SHAP, an efficient algorithm that reduces the computational complexity of calculating Shapley values for quadratic losses to polynomial time.	Zhongli Jiang; Min Zhang; Dabao Zhang;
204	Distributional Reinforcement Learning with Dual Expectile-Quantile Regression Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Motivated by the efficiency of $L_2$-based learning, we propose to jointly learn expectiles and quantiles of the return distribution in a way that allows efficient learning while keeping an estimate of the full distribution of returns.	Sami Jullien; Romain Deffayet; Jean-Michel Renders; Paul Groth; Maarten de Rijke;
205	Provably Adaptive Average Reward Reinforcement Learning for Metric Spaces Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We study infinite-horizon average-reward reinforcement learning (RL) for Lipschitz MDPs, a broad class that subsumes several important classes such as linear and RKHS MDPs, function approximation frameworks, and develop an adaptive algorithm $\text{ZoRL}$ with regret bounded as $\mathcal{O}\big(T^{1 – d_{\text{eff.}}	Avik Kar; Rahul Singh;
206	ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes a new federated sampling algorithm called Error Feedback Langevin algorithms (ELF).	Avetik Karagulyan; Peter Richt�rik;
207	Enumerating Optimal Cost-Constrained Adjustment Sets Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present algorithms for enumerating valid and minimal adjustment sets up to a specified cost, ordered by their proximity to outcome variables, which coincides with estimator variance.	Batya Kenig;
208	Accurate and Scalable Stochastic Gaussian Process Regression Via Learnable Coreset-based Variational Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a novel stochastic variational inference method for Gaussian process ($\mathcal{GP}$) regression, by deriving a posterior over a learnable set of coresets: i.e., over pseudo-input/output, weighted pairs.	Mert Ketenci; Adler J Perotte; No�mie Elhadad; I�igo Urteaga;
209	Efficiently Escaping Saddle Points for Policy Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a variance-reduced second-order method that uses second-order information in the form of Hessian vector products (HVP) and converges to an approximate second-order stationary point (SOSP) with sample complexity of $\tilde{O}(\epsilon^{-3})$.	Mohammadsadegh Khorasani; Saber Salehkaleybar; Negar Kiyavash; Niao He; Matthias Grossglauser;
210	CP$^2$: Leveraging Geometry for Conformal Prediction Via Canonicalization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While CP endows prediction models with post-hoc uncertainty quantification and formal coverage guarantees, their practicality breaks under distribution shifts that deteriorate model performance. To address this issue, we propose integrating geometric information-such as geometric pose-into the conformal procedure to reinstate its guarantees and ensure robustness under geometric shifts.	Putri A Van der Linden; Alexander Timans; Erik J Bekkers;
211	Weak to Strong Learning from Aggregate Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: A weak learner in this context is one which has at a constant accuracy $ < 1$ on the training bags, while a strong learner’s accuracy can be arbitrarily close to $1$. We study the problem of using a weak learner on such training bags with aggregate labels to obtain a strong learner.	Yukti Makhija; Rishi Saket;
212	SALSA: A Secure, Adaptive and Label-Agnostic Scalable Algorithm for Machine Unlearning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce SALSA, a Secure, Adaptive, Label-Agnostic, Scalable Algorithm for efficient and robust machine unlearning tailored to classification tasks in MLaaS.	Owais Makroo; Atif Hassan; Swanand Khare;
213	MindFlayer SGD: Efficient Parallel SGD in The Presence of Heterogeneous and Random Worker Compute Times Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While some parallel SGD algorithms achieve optimal performance under deterministic but heterogeneous delays, their effectiveness diminishes when compute times are random-a scenario not explicitly addressed in their design. To bridge this gap, we introduce MindFlayer SGD, a novel parallel SGD method specifically designed to handle stochastic and heterogeneous compute times.	Arto Maranjyan; Omar Shaikh Omar; Peter Richt�rik;
214	Improved Variational Inference in Discrete VAEs Using Error Correcting Codes Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This work introduces a novel method to improve inference in discrete Variational Autoencoders by reframing the inference problem through a generative perspective.	Mar�a Mart�nez-Garc�a; Grace Villacr�s; David Mitchell; Pablo M. Olmos;
215	When Extragradient Meets PAGE: Bridging Two Giants to Boost Variational Inequalities Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a new stochastic variance reduced algorithm for solving stochastic variational inequalities.	Gleb Molodtsov; Valery Parfenov; Egor Petrov; Evseev Grigoriy; Daniil Medyakov; Aleksandr Beznosikov;
216	Relational Causal Discovery with Latent Confounders Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Similarly, existing relational causal discovery algorithms assume causal sufficiency, which is unrealistic for many real-world datasets. To address this gap, we propose RelFCI, a sound and complete causal discovery algorithm for relational data with latent confounders.	Matteo Negro; Andrea Piras; Ragib Ahsan; David Arbour; Elena Zheleva;
217	Multiple Wasserstein Gradient Descent Algorithm for Multi-Objective Distributional Optimization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: This type of Multi-Objective Distributional Optimization commonly arises in machine learning and statistics, with applications in areas such as multiple target sampling, multi-task learning, and multi-objective generative modeling. To solve this problem, we propose an iterative particle-based algorithm, which we call Muliple Wasserstein Gradient Descent (MWGraD), which constructs a flow of intermediate empirical distributions, each being represented by a set of particles, which gradually minimize the multiple objective functionals simultaneously.	Hai Dai Nguyen; Hiroshi Mamitsuka; Atsuyoshi Nakamura;
218	Stochastic Embeddings : A Probabilistic and Geometric Analysis of Out-of-Distribution Behavior Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We identify a geometric phenomenon in the embedding space: in-distribution (ID) data show higher variance than OoD data under stochastic perturbations.	Anthony Nguyen; Emanuel Aldea; Sylvie Le H�garat-Mascle; Renaud Lustrat;
219	Probability-Raising Causality for Uncertain Parametric Markov Decision Processes with PAC Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes a method to identify potential causes of undesired behaviors in an uncertain parametric MDP (upMDP) using parameter sampling, model checking, and a set covering for the samples.	Ryohei Oura; Yuji Ito;
220	A Trust-Region Method for Graphical Stein Variational Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the optimization techniques employed in existing SVI methods struggle to address problems in which the target distribution is high-dimensional, poorly-conditioned, or non-convex, which severely limits the range of their practical applicability. In this paper, we propose a novel trust-region optimization approach for SVI that successfully addresses each of these challenges.	Liam Pavlovic; David M Rosen;
221	Learning with Confidence Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We formally axiomatize what it means to learn with confidence, give two canonical ways of measuring confidence on a continuum, and prove that confidence can always be represented in this way.	Oliver Ethan Richardson;
222	Reparameterizing Hybrid Markov Logic Networks to Handle Covariate-Shift in Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We evaluate our approach on Graph Neural Networks and show that our approach outperforms state-of-the-art methods that combine relational knowledge with DNN embeddings when we introduce covariate shifts in the embeddings.	Anup Shakya; Abisha Thapa Magar; Somdeb Sarkhel; Deepak Venugopal;
223	Minimax Optimal Nonsmooth Nonparametric Regression Via Fractional Laplacian Eigenmaps Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We develop minimax optimal estimators for nonparametric regression methods when the true regression function lies in an $L_2$-fractional Sobolev space with order $s\in (0,1)$.	Zhaoyang Shi; Krishna Balasubramanian; Wolfgang Polonik;
224	Learning from Label Proportions and Covariate-shifted Instances Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Fully supervised covariate shifted data often has useful training signals and the goal is to leverage them for better predictive performance in the hybrid LLP setting. To achieve this, we develop methods for hybrid LLP which naturally incorporate the target bag-labels along with the source instance-labels, in the domain adaptation framework.	Sagalpreet Singh; Navodita Sharma; Shreyas Havaldar; Rishi Saket; Aravindan Raghuveer;
225	Pure and Strong Nash Equilibrium Computation in Compactly Representable Aggregate Games Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: For broad classes, we provide several novel and efficient aggregate-space algorithms for recognizing an SNE and deciding the existence of an SNE.	Jared Soundy; Mohammad T. Irfan; Hau Chan;
226	Nonparametric Bayesian Inference of Item-level Features in Classifier Combination Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We develop a Bayesian model that can infer generic item features by modeling item feature membership as distributed according to an Indian Buffet Process.	Patrick Stinson; Nikolaus Kriegeskorte;
227	Transparent Trade-offs Between Properties of Explanations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The ideal balance of trade-offs between properties tends to vary across different tasks and users. Motivated by these varying needs, we aim to find explanations that make optimal trade-offs while allowing for transparent control over the balance between different properties.	Hiwot Belay Tadesse; Alihan H�y�k; Yaniv Yacoby; Weiwei Pan; Finale Doshi-Velez;
228	Complete Characterization for Adjustment in Summary Causal Graphs of Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We study this problem, considering multiple interventions, in the context of time series when only an abstraction of the true causal graph, in the form of a summary causal graph, is available. We propose in particular both necessary and sufficient conditions for the adjustment criterion, which we show is complete in this setting, and provide a pseudo-linear algorithm to decide whether the query is identifiable or not.	Cl�ment Yvernes; Emilie Devijver; Eric Gaussier;
229	Instance-Wise Monotonic Calibration By Constrained Transformation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a family of novel monotonic post-hoc calibration methods, which employs a constrained calibration map parameterized linearly with respect to the number of classes.	Yunrui Zhang; Gustavo Enrique Batista; Salil S. Kanhere;