AISTATS 2024

547 papers

Optimal Transport for Measures with Noisy Tree Metric

Tam Le, Truyen Nguyen, Kenji Fukumizu

Learning Fair Division from Bandit Feedback

Hakuei Yamada, Junpei Komiyama, Kenshi Abe, Atsushi Iwasaki

Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes

Washim U. Mondal, Vaneet Aggarwal

Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers

Pim de Haan, Taco Cohen, Johann Brehmer

The Relative Gaussian Mechanism and its Application to Private Gradient Descent

Hadrien Hendrikx, Paul Mangold, Aurélien Bellet

Learning the Pareto Set Under Incomplete Preferences: Pure Exploration in Vector Bandits

Efe Mert Karagözlü, Yaşar Cahit Yıldırım, Cağın Ararat, Cem Tekin

Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition

Long-Fei Li, Peng Zhao, Zhi-Hua Zhou

A Greedy Approximation for k-Determinantal Point Processes

Julia Grosse, Rahel Fischer, Roman Garnett, Philipp Hennig

Stochastic Multi-Armed Bandits with Strongly Reward-Dependent Delays

Yifu Tang, Yingfei Wang, Zeyu Zheng

Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks

Ahmad Rashid, Serena Hacker, Guojun Zhang, Agustinus Kristiadi, Pascal Poupart

Reward-Relevance-Filtered Linear Offline Reinforcement Learning

Angela Zhou

On the connection between Noise-Contrastive Estimation and Contrastive Divergence

Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten

Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures

Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi

Density-Regression: Efficient and Distance-aware Deep Regressor for Uncertainty Estimation under Distribution Shifts

Ha Manh Bui, Anqi Liu

Faithful graphical representations of local independence

Søren W. Mogensen

Pessimistic Off-Policy Multi-Objective Optimization

Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain, Branislav Kveton

Spectrum Extraction and Clipping for Implicitly Linear Layers

Ali Ebrahimpour Boroojeny, Matus Telgarsky, Hari Sundaram

SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic Bandits

Subhojyoti Mukherjee, Qiaomin Xie, Josiah P Hanna, Robert Nowak

Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias

Yu Yang, Eric Gan, Gintare Karolina Dziugaite, Baharan Mirzasoleiman

Online Learning of Decision Trees with Thompson Sampling

Ayman Chaouki, Jesse Read, Albert Bifet

Unveiling Latent Causal Rules: A Temporal Point Process Approach for Abnormal Event Explanation

Yiling Kuang, Chao Yang, Yang Yang, Shuang Li

Estimating treatment effects from single-arm trials via latent-variable modeling

Manuel Haussmann, Tran Minh Son Le, Viivi Halla-aho, Samu Kurki, Jussi Leinonen

Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods

Jiaxin Zhang, Kamalika Das, Sricharan Kumar

Fast Minimization of Expected Logarithmic Loss via Stochastic Dual Averaging

Chung-En Tsai, Hao-Chung Cheng, Yen-Huan Li

Non-Convex Joint Community Detection and Group Synchronization via Generalized Power Method

Sijin Chen, Xiwei Cheng, Anthony Man-Cho So

Multi-Level Symbolic Regression: Function Structure Learning for Multi-Level Data

Kei Sen Fong, Mehul Motani

Conformalized Semi-supervised Random Forest for Classification and Abnormality Detection

Yujin Han, Mingwenchan Xu, Leying Guan

Contextual Directed Acyclic Graphs

Ryan Thompson, Edwin V. Bonilla, Robert Kohn

End-to-end Feature Selection Approach for Learning Skinny Trees

Shibal Ibrahim, Kayhan Behdin, Rahul Mazumder

Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methods

Davoud Ataee Tarzanagh, Parvin Nazari, Bojian Hou, Li Shen, Laura Balzano

Multivariate Time Series Forecasting By Graph Attention Networks With Theoretical Guarantees

Zhi Zhang, Weijian Li, Han Liu

Electronic Medical Records Assisted Digital Clinical Trial Design

Xinrui Ruan, Jingshen Wang, Yingfei Wang, Waverly Wei

Near-optimal Per-Action Regret Bounds for Sleeping Bandits

Quan M. Nguyen, Nishant Mehta

Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification

Shivvrat Arya, Yu Xiang, Vibhav Gogate

Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients

Chris J. Cundy, Rishi Desai, Stefano Ermon

Adaptive and non-adaptive minimax rates for weighted Laplacian-Eigenmap based nonparametric regression

Zhaoyang Shi, Krishna Balasubramanian, Wolfgang Polonik

Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models

Shivvrat Arya, Tahrima Rahman, Vibhav Gogate

Conditional Adjustment in a Markov Equivalence Class

Sara LaPlante, Emilija Perkovic

Any-dimensional equivariant neural networks

Eitan Levin, Mateo Diaz

On the Model-Misspecification in Reinforcement Learning

Yunfan Li, Lin Yang

Privacy-Preserving Decentralized Actor-Critic for Cooperative Multi-Agent Reinforcement Learning

Maheed H. Ahmed, Mahsa Ghasemi

Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling

Arman Adibi, Nicolò Dal Fabbro, Luca Schenato, Sanjeev Kulkarni, H. Vincent Poor

Policy Evaluation for Reinforcement Learning from Human Feedback: A Sample Complexity Analysis

Zihao Li, Xiang Ji, Minshuo Chen, Mengdi Wang

Invariant Aggregator for Defending against Federated Backdoor Attacks

Xiaoyang Wang, Dimitrios Dimitriadis, Sanmi Koyejo, Shruti Tople

Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation

Zhishuai Liu, Pan Xu

From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach

Tuan Nguyen, Hirotada Honda, Takashi Sano, Vinh Nguyen, Shugo Nakamura

Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression

Sijin Chen, Zhize Li, Yuejie Chi

PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model

Abhinav Chakraborty, Anirban Chatterjee, Abhinandan Dalal

Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts

Huy Nguyen, TrungTin Nguyen, Khai Nguyen, nhật Hồ

On Parameter Estimation in Deviated Gaussian Mixture of Experts

Huy Nguyen, Khai Nguyen, nhật Hồ

Strategic Usage in a Multi-Learner Setting

Eliot Shekhtman, Sarah Dean

Nonparametric Automatic Differentiation Variational Inference with Spline Approximation

Yuda Shao, Shan N Yu, Tianshu Feng

Graph Pruning for Enumeration of Minimal Unsatisfiable Subsets

Panagiotis Lymperopoulos, Liping Liu

Graph fission and cross-validation

James Leiner, Aaditya Ramdas

Achieving Group Distributional Robustness and Minimax Group Fairness with Interpolating Classifiers

Natalia L. Martinez, Martin A. Bertran, Guillermo Sapiro

Large-Scale Gaussian Processes via Alternating Projection

Kaiwen Wu, Jonathan Wenger, Haydn T Jones, Geoff Pleiss, Jacob Gardner

Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo

Haoyang Zheng, Wei Deng, Christian Moya, Guang Lin

Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits

Arnab Maiti, Ross Boczar, Kevin Jamieson, Lillian Ratliff

Maximum entropy GFlowNets with soft Q-learning

Sobhan Mohammadpour, Emmanuel Bengio, Emma Frejinger, Pierre-Luc Bacon

Continual Domain Adversarial Adaptation via Double-Head Discriminators

Yan Shen, Zhanghexuan Ji, Chunwei Ma, Mingchen Gao

BlockBoost: Scalable and Efficient Blocking through Boosting

Thiago Ramos, Rodrigo Loro Schuller, Alex Akira Okuno, Lucas Nissenbaum, Roberto I Oliveira

Surrogate Bayesian Networks for Approximating Evolutionary Games

Vincent Hsiao, Dana S Nau, Bobak Pezeshki, Rina Dechter

Efficient Data Shapley for Weighted Nearest Neighbor Algorithms

Jiachen T. Wang, Prateek Mittal, Ruoxi Jia

Bandit Pareto Set Identification: the Fixed Budget Setting

Cyrille KONE, Emilie Kaufmann, Laura Richert

Bayesian Online Learning for Consensus Prediction

Samuel Showalter, Alex J Boyd, Padhraic Smyth, Mark Steyvers

Minimizing Convex Functionals over Space of Probability Measures via KL Divergence Gradient Flow

Rentian Yao, Linjun Huang, Yun Yang

Learning Cartesian Product Graphs with Laplacian Constraints

Changhao Shi, Gal Mishne

Can Probabilistic Feedback Drive User Impacts in Online Platforms?

Jessica Dai, Bailey Flanigan, Nika Haghtalab, Meena Jagadeesan, Chara Podimata

Lexicographic Optimization: Algorithms and Stability

Jacob A. Abernethy, Robert Schapire, Umar Syed

Learning Adaptive Kernels for Statistical Independence Tests

Yixin Ren, Yewei Xia, Hao Zhang, Jihong Guan, Shuigeng Zhou

Conformal Contextual Robust Optimization

Yash P. Patel, Sahana Rayan, Ambuj Tewari

Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective

Sanath Kumar Krishnamurthy, Adrienne M Propp, Susan Athey

Optimal Zero-Shot Detector for Multi-Armed Attacks

Federica Granese, Marco Romanelli, Pablo Piantanida

Federated Experiment Design under Distributed Differential Privacy

Wei-Ning Chen, Graham Cormode, Akash Bharadwaj, Peter Romov, Ayfer Ozgur

Functional Graphical Models: Structure Enables Offline Data-Driven Optimization

Kuba Grudzien, Masatoshi Uehara, Sergey Levine, Pieter Abbeel

Multi-Resolution Active Learning of Fourier Neural Operators

Shibo Li, Xin Yu, Wei Xing, Robert Kirby, Akil Narayan

Mixed variational flows for discrete variables

Gian C. Diluvi, Benjamin Bloem-Reddy, Trevor Campbell

An Impossibility Theorem for Node Embedding

T. Mitchell Roddenberry, Yu Zhu, Santiago Segarra

Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels

Da Long, Wei Xing, Aditi Krishnapriyan, Robert Kirby, Shandian Zhe

Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data

Miguel Fuentes, Brett C. Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon

Online Learning in Contextual Second-Price Pay-Per-Click Auctions

Mengxiao Zhang, Haipeng Luo

Learning Safety Constraints from Demonstrations with Unknown Rewards

David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause

Approximate Leave-one-out Cross Validation for Regression with ℓ_1 Regularizers

Arnab Auddy, Haolin Zou, Kamiar Rahnamarad, Arian Maleki

Simulating weighted automata over sequences and trees with transformers

Michael Rizvi-Martel, Maude Lizaire, Clara Lacroce, Guillaume Rabusseau

Implicit Regularization in Deep Tucker Factorization: Low-Rankness via Structured Sparsity

Kais Hariz, Hachem Kadri, Stéphane Ayache, Maher Moakher, Thierry Artières

Analysis of Kernel Mirror Prox for Measure Optimization

Pavel Dvurechensky, Jia-Jie Zhu

EM for Mixture of Linear Regression with Clustered Data

Amirhossein Reisizadeh, Khashayar Gatmiry, Asuman Ozdaglar

Stochastic Approximation with Biased MCMC for Expectation Maximization

Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob Gardner

Sum-max Submodular Bandits

Stephen U. Pasteris, Alberto Rumi, Fabio Vitale, Nicolò Cesa-bianchi

General Identifiability and Achievability for Causal Representation Learning

Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Ali Tajer

A Doubly Robust Approach to Sparse Reinforcement Learning

Wonyoung Kim, Garud Iyengar, Assaf Zeevi

MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization

Nguyen Hoang Khoi Do, Tanmoy Chowdhury, Chen Ling, Liang Zhao, My T. Thai

Backward Filtering Forward Deciding in Linear Non-Gaussian State Space Models

Yun-Peng Li, Hans-Andrea Loeliger

Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers

Krzysztof Choromanski, Shanda Li, Valerii Likhosherstov, Kumar Avinava Dubey, Shengjie Luo

Imposing Fairness Constraints in Synthetic Data Generation

Mahed Abroshan, Andrew Elliott, Mohammad Mahdi Khalili

Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent

Pratik Patil, Yuchen Wu, Ryan Tibshirani

GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models

Tolga Dimlioglu, Anna Choromanska

Agnostic Multi-Robust Learning using ERM

Saba Ahmadi, Avrim Blum, Omar Montasser, Kevin M Stangl

Monotone Operator Theory-Inspired Message Passing for Learning Long-Range Interaction on Graphs

Justin M. Baker, Qingsong Wang, Martin Berzins, Thomas Strohmer, Bao Wang

Optimising Distributions with Natural Gradient Surrogates

Jonathan So, Richard E. Turner