AISTATS 2023

496 papers

Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach

Yunzhe Zhou, Zhengling Qi, Chengchun Shi, Lexin Li

Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection

Weilin Cong, Mehrdad Mahdavi

Learning with Partial Forgetting in Modern Hopfield Networks

Toshihiro Ota, Ikuro Sato, Rei Kawakami, Masayuki Tanaka, Nakamasa Inoue

Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout

Chen Dun, Mirian Hipolito, Chris Jermaine, Dimitrios Dimitriadis, Anastasios Kyrillidis

Scalable Spectral Clustering with Group Fairness Constraints

Ji Wang, Ding Lu, Ian Davidson, Zhaojun Bai

Reconstructing Training Data from Model Gradient, Provably

Zihan Wang, Jason Lee, Qi Lei

Precision Recall Cover: A Method For Assessing Generative Models

Fasil Cheema, Ruth Urner

Provably Efficient Model-Free Algorithms for Non-stationary CMDPs

Honghao Wei, Arnob Ghosh, Ness Shroff, Lei Ying, Xingyu Zhou

LOFT: Finding Lottery Tickets through Filter-wise Training

Qihan Wang, Chen Dun, Fangshuo Liao, Chris Jermaine, Anastasios Kyrillidis

A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets

Hideaki Ishibashi, Masayuki Karasuyama, Ichiro Takeuchi, Hideitsu Hino

Distributionally Robust Policy Gradient for Offline Contextual Bandits

Zhouhao Yang, Yihong Guo, Pan Xu, Anqi Liu, Animashree Anandkumar

Revisiting Fair-PAC Learning and the Axioms of Cardinal Welfare

Cyrus Cousins

NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning

Muralikrishnna G Sethuraman, Romain Lopez, Rahul Mohan, Faramarz Fekri, Tommaso Biancalani

Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning

Volodymyr Tkachuk, Seyed Alireza Bakhtiari, Johannes Kirschner, Matej Jusup, Ilija Bogunovic

Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion

Haotian Ju, Dongyue Li, Aneesh Sharma, Hongyang R. Zhang

Discrete Langevin Samplers via Wasserstein Gradient Flow

Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, Dale Schuurmans

Dueling RL: Reinforcement Learning with Trajectory Preferences

Aadirupa Saha, Aldo Pacchiano, Jonathan Lee

Continuous-Time Decision Transformer for Healthcare Applications

Zhiyue Zhang, Hongyuan Mei, Yanxun Xu

Nonstationary Bandit Learning via Predictive Sampling

Yueyang Liu, Benjamin Van Roy, Kuang Xu

Distill n’ Explain: explaining graph neural networks using simple surrogates

Tamara Pereira, Erik Nascimento, Lucas E. Resck, Diego Mesquita, Amauri Souza

Dimensionality Collapse: Optimal Measurement Selection for Low-Error Infinite-Horizon Forecasting

Helmuth Naumer, Farzad Kamalabadi

Minimax Nonparametric Two-Sample Test under Adversarial Losses

Rong Tang, Yun Yang

Stochastic Tree Ensembles for Estimating Heterogeneous Effects

Nikolay Krantsevich, Jingyu He, P. Richard Hahn

Delayed Feedback in Generalised Linear Bandits Revisited

Benjamin Howson, Ciara Pike-Burke, Sarah Filippi

Optimism and Delays in Episodic Reinforcement Learning

Benjamin Howson, Ciara Pike-Burke, Sarah Filippi

Robust and Agnostic Learning of Conditional Distributional Treatment Effects

Nathan Kallus, Miruna Oprescu

Risk Bounds on Aleatoric Uncertainty Recovery

Yikai Zhang, Jiahe Lin, Fengpei Li, Yeshaya Adler, Kashif Rasul

Reinforcement Learning for Adaptive Mesh Refinement

Jiachen Yang, Tarik Dzanic, Brenden Petersen, Jun Kudo, Ketan Mittal

Average Adjusted Association: Efficient Estimation with High Dimensional Confounders

Sung Jae Jun, Sokbae Lee

High Probability Bounds for Stochastic Continuous Submodular Maximization

Evan Becker, Jingdong Gao, Ted Zadouri, Baharan Mirzasoleiman

Stochastic Mirror Descent for Large-Scale Sparse Recovery

Sasila Ilandarideva, Yannis Bekri, Anatoli Iouditski, Vianney Perchet

Heavy Sets with Applications to Interpretable Machine Learning Diagnostics

Dmitry Malioutov, Sanjeeb Dash, Dennis Wei

Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification

Yuqing Hu, Stephane Pateux, Vincent Gripon

Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions

Zeshan Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David Sontag

Cooperative Inverse Decision Theory for Uncertain Preferences

Zachary Robertson, Hantao Zhang, Sanmi Koyejo

Variational Boosted Soft Trees

Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov

Private Non-Convex Federated Learning Without a Trusted Server

Andrew Lowy, Ali Ghafelebashi, Meisam Razaviyayn

Differentially Private Matrix Completion through Low-rank Matrix Factorization

Lingxiao Wang, Boxin Zhao, Mladen Kolar

Active Membership Inference Attack under Local Differential Privacy in Federated Learning

Truc Nguyen, Phung Lai, Khang Tran, NhatHai Phan, My T. Thai

A Contrastive Approach to Online Change Point Detection

Nikita Puchkin, Valeriia Shcherbakova

Symmetric (Optimistic) Natural Policy Gradient for Multi-Agent Learning with Parameter Convergence

Sarath Pattathil, Kaiqing Zhang, Asuman Ozdaglar

Boosted Off-Policy Learning

Ben London, Levi Lu, Ted Sandler, Thorsten Joachims

Faithful Heteroscedastic Regression with Neural Networks

Andrew Stirn, Harm Wessels, Megan Schertzer, Laura Pereira, Neville Sanjana

A Faster Sampler for Discrete Determinantal Point Processes

Simon Barthelmé, Nicolas Tremblay, Pierre-Olivier Amblard

TabLLM: Few-shot Classification of Tabular Data with Large Language Models

Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang

Is interpolation benign for random forest regression?

Ludovic Arnould, Claire Boyer, Erwan Scornet

Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments

Vincent Liu, Yash Chandak, Philip Thomas, Martha White

Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation

Gandharv Patil, Prashanth L.A., Dheeraj Nagaraj, Doina Precup

A Tighter Problem-Dependent Regret Bound for Risk-Sensitive Reinforcement Learning

Xiaoyan Hu, Ho-fung Leung

Smoothly Giving up: Robustness for Simple Models

Tyler Sypherd, Nathaniel Stromberg, Richard Nock, Visar Berisha, Lalitha Sankar

Adversarial Random Forests for Density Estimation and Generative Modeling

David S. Watson, Kristin Blesch, Jan Kapar, Marvin N. Wright

Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms

Vincent Plassier, Eric Moulines, Alain Durmus

Unified Perspective on Probability Divergence via the Density-Ratio Likelihood: Bridging KL-Divergence and Integral Probability Metrics

Masahiro Kato, Masaaki Imaizumi, Kentaro Minami

Efficient fair PCA for fair representation learning

Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar

Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery

Quan Nguyen, Roman Garnett

Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel

Jonas Wacker, Ruben Ohana, Maurizio Filippone

A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization

Yutong Dai, Guanyi Wang, Frank E. Curtis, Daniel P. Robinson

Manifold Restricted Interventional Shapley Values

Muhammad Faaiz Taufiq, Patrick Blöbaum, Lenon Minorics

Asymptotic Bayes risk of semi-supervised multitask learning on Gaussian mixture

Minh-Toan Nguyen, Romain Couillet

Privacy-preserving Sparse Generalized Eigenvalue Problem

Lijie Hu, Zihang Xiang, Jiabin Liu, Di Wang

Bounding Evidence and Estimating Log-Likelihood in VAE

Łukasz Struski, Marcin Mazur, Paweł Batorski, Przemysław Spurek, Jacek Tabor

Optimal Contextual Bandits with Knapsacks under Realizability via Regression Oracles

Yuxuan Han, Jialin Zeng, Yang Wang, Yang Xiang, Jiheng Zhang

Gaussian Processes on Distributions based on Regularized Optimal Transport

Francois Bachoc, Louis Béthune, Alberto Gonzalez-Sanz, Jean-Michel Loubes

Adaptation to Misspecified Kernel Regularity in Kernelised Bandits

Yusha Liu, Aarti Singh

Neural Simulated Annealing

Alvaro H.C. Correia, Daniel E. Worrall, Roberto Bondesan

On Generalization of Decentralized Learning with Separable Data

Hossein Taheri, CHRISTOS THRAMPOULIDIS

High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent

Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi

A Sea of Words: An In-Depth Analysis of Anchors for Text Data

Gianluigi Lopardo, Frederic Precioso, Damien Garreau

Multi-task Representation Learning with Stochastic Linear Bandits

Leonardo Cella, Karim Lounici, Grégoire Pacreau, Massimiliano Pontil

Coordinate Descent for SLOPE

Johan Larsson, Quentin Klopfenstein, Mathurin Massias, Jonas Wallin

Mediated Uncoupled Learning and Validation with Bregman Divergences: Loss Family with Maximal Generality

Ikko Yamane, Yann Chevaleyre, Takashi Ishida, Florian Yger

Approximating a RUM from Distributions on k-Slates

Flavio Chierichetti, Mirko Giacchini, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins

Equivariant Representation Learning via Class-Pose Decomposition

Giovanni Luca Marchetti, Gustaf Tegnér, Anastasiia Varava, Danica Kragic

A Statistical Learning Take on the Concordance Index for Survival Analysis

Kevin Elgui, Alex Nowak, Geneviève Robin

Computing Abductive Explanations for Boosted Trees

Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski

Feasible Recourse Plan via Diverse Interpolation

Duy Nguyen, Ngoc Bui, Viet Anh Nguyen

Neural Discovery of Permutation Subgroups

Pavan Karjol, Rohan Kashyap, Prathosh AP

Fair Representation Learning with Unreliable Labels

YIXUAN ZHANG, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen

Nyström Method for Accurate and Scalable Implicit Differentiation

Ryuichiro Hataya, Makoto Yamada

A Unified Perspective on Regularization and Perturbation in Differentiable Subset Selection

Xiangqian Sun, Cheuk Hang Leung, Yijun Li, Qi Wu

Mean Parity Fair Regression in RKHS

Shaokui Wei, Jiayin Liu, Bing Li, Hongyuan Zha

Isotropic Gaussian Processes on Finite Spaces of Graphs

Viacheslav Borovitskiy, Mohammad Reza Karimi, Vignesh Ram Somnath, Andreas Krause

Oblivious near-optimal sampling for multidimensional signals with Fourier constraints

Xingyu Xu, Yuantao Gu

Temporal Graph Neural Networks for Irregular Data

Joel Oskarsson, Per Sidén, Fredrik Lindsten

Learning Sparse Graphon Mean Field Games

Christian Fabian, Kai Cui, Heinz Koeppl

Toward Fairness in Text Generation via Mutual Information Minimization based on Importance Sampling

Rui Wang, Pengyu Cheng, Ricardo Henao

Distributed Offline Policy Optimization Over Batch Data

Han Shen, Songtao Lu, Xiaodong Cui, Tianyi Chen

Subset verification and search algorithms for causal DAGs

Davin Choo, Kirankumar Shiragur

Conjugate Gradient Method for Generative Adversarial Networks

Hiroki Naganuma, Hideaki Iiduka

Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data

Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng, Wenlong Ji, James Zou

Does Label Differential Privacy Prevent Label Inference Attacks?

Ruihan Wu, Jin Peng Zhou, Kilian Q. Weinberger, Chuan Guo

Piecewise Stationary Bandits under Risk Criteria

Sujay Bhatt, Guanhua Fang, Ping Li

Learning While Scheduling in Multi-Server Systems With Unknown Statistics: MaxWeight with Discounted UCB

Zixian Yang, R. Srikant, Lei Ying

The communication cost of security and privacy in federated frequency estimation

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

Universal Agent Mixtures and the Geometry of Intelligence

Samuel Allen Alexander, David Quarel, Len Du, Marcus Hutter

Unsupervised representation learning with recognition-parametrised probabilistic models

William I. Walker, Hugo Soulat, Changmin Yu, Maneesh Sahani

Bayesian Strategy-Proof Facility Location via Robust Estimation

Emmanouil Zampetakis, Fred Zhang

The Lauritzen-Chen Likelihood For Graphical Models

Ilya Shpitser

Likelihood-Based Generative Radiance Field with Latent Space Energy-Based Model for 3D-Aware Disentangled Image Representation

Yaxuan Zhu, Jianwen Xie, Ping Li

On Universal Portfolios with Continuous Side Information

Alankrita Bhatt, J. Jon Ryu, Young-Han Kim