AISTATS 2023

496 papers

Exploration in Reward Machines with Low Regret

Hippolyte Bourel, Anders Jonsson, Odalric-ambrym Maillard, Mohammad Sadegh Talebi

Active Learning for Single Neuron Models with Lipschitz Non-Linearities

Aarshvi Gajjar, Christopher Musco, Chinmay Hegde

Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Models

Naoya Takeishi, Alexandros Kalousis

A Multi-Task Gaussian Process Model for Inferring Time-Varying Treatment Effects in Panel Data

Yehu Chen, Annamaria Prati, Jacob Montgomery, Roman Garnett

Sampling From a Schrödinger Bridge

Austin Stromme

FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery

Xinyi Xu, Zhaoxuan Wu, Arun Verma, Chuan Sheng Foo, Bryan Kian Hsiang Low

Provably Efficient Reinforcement Learning via Surprise Bound

Hanlin Zhu, Ruosong Wang, Jason Lee

Autoencoded sparse Bayesian in-IRT factorization, calibration, and amortized inference for the Work Disability Functional Assessment Battery

Joshua C. Chang, Carson C. Chow, Julia Porcino

The ELBO of Variational Autoencoders Converges to a Sum of Entropies

Simon Damm, Dennis Forster, Dmytro Velychko, Zhenwen Dai, Asja Fischer

On-Demand Communication for Asynchronous Multi-Agent Bandits

Yu-Zhen Janice Chen, Lin Yang, Xuchuang Wang, Xutong Liu, Mohammad Hajiesmaili

Deep Joint Source-Channel Coding with Iterative Source Error Correction

Changwoo Lee, Xiao Hu, Hun-Seok Kim

ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits

Thomas Kleine Buening, Aadirupa Saha

Sample Efficiency of Data Augmentation Consistency Regularization

Shuo Yang, Yijun Dong, Rachel Ward, Inderjit S. Dhillon, Sujay Sanghavi

Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior

Yohan Jung, Jinkyoo Park

On the bias of K-fold cross validation with stable learners

Anass Aghbalou, Anne Sabourin, François Portier

Sparse Bayesian optimization

Sulin Liu, Qing Feng, David Eriksson, Benjamin Letham, Eytan Bakshy

Learning from Multiple Sources for Data-to-Text and Text-to-Data

Song Duong, Alberto Lumbreras, Mike Gartrell, Patrick Gallinari

MARS: Masked Automatic Ranks Selection in Tensor Decompositions

Maxim Kodryan, Dmitry Kropotov, Dmitry Vetrov

Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition

Lukang Sun, Avetik Karagulyan, Peter Richtarik

Towards Balanced Representation Learning for Credit Policy Evaluation

Yiyan Huang, Cheuk Hang Leung, Shumin Ma, Zhiri Yuan, Qi Wu

Transport Elliptical Slice Sampling

Alberto Cabezas, Christopher Nemeth

ASkewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks

Louis Leconte, Sholom Schechtman, Eric Moulines

Noise-Aware Statistical Inference with Differentially Private Synthetic Data

Ossi Räisä, Joonas Jälkö, Samuel Kaski, Antti Honkela

No-regret Sample-efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities

Sebastian Shenghong Tay, Quoc Phong Nguyen, Chuan Sheng Foo, Bryan Kian Hsiang Low

To Impute or not to Impute? Missing Data in Treatment Effect Estimation

Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar

Robust Variational Autoencoding with Wasserstein Penalty for Novelty Detection

Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman

Online Learning for Non-monotone DR-Submodular Maximization: From Full Information to Bandit Feedback

Qixin Zhang, Zengde Deng, Zaiyi Chen, Kuangqi Zhou, Haoyuan Hu

Membership Inference Attacks against Synthetic Data through Overfitting Detection

Boris van Breugel, Hao Sun, Zhaozhi Qian, Mihaela van der Schaar

T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression

Yuchao Qin, Mihaela van der Schaar, Changhee Lee

Minority Oversampling for Imbalanced Data via Class-Preserving Regularized Auto-Encoders

Arnab Kumar Mondal, Lakshya Singhal, Piyush Tiwary, Parag Singla, Prathosh AP

A Bregman Divergence View on the Difference-of-Convex Algorithm

Oisin Faust, Hamza Fawzi, James Saunderson

Connectivity-contrastive learning: Combining causal discovery and representation learning for multimodal data

Hiroshi Morioka, Aapo Hyvärinen

A Finite Sample Complexity Bound for Distributionally Robust Q-learning

Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou

Singular Value Representation: A New Graph Perspective On Neural Networks

Dan Meller, Nicolas Berkouk

The Lie-Group Bayesian Learning Rule

Eren Mehmet Kiral, Thomas Moellenhoff, Mohammad Emtiyaz Khan

Generative Oversampling for Imbalanced Data via Majority-Guided VAE

Qingzhong Ai, Pengyun Wang, LIRONG HE, Liangjian Wen, Lujia Pan

Mode-constrained Model-based Reinforcement Learning via Gaussian Processes

Aidan Scannell, Carl Henrik Ek, Arthur Richards

No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution

Mengxiao Zhang, Shi Chen, Haipeng Luo, Yingfei Wang

Byzantine-Robust Online and Offline Distributed Reinforcement Learning

Yiding Chen, Xuezhou Zhang, Kaiqing Zhang, Mengdi Wang, Xiaojin Zhu

Online Learning for Traffic Routing under Unknown Preferences

Devansh Jalota, Karthik Gopalakrishnan, Navid Azizan, Ramesh Johari, Marco Pavone

An Unpooling Layer for Graph Generation

Yinglong Guo, Dongmian Zou, Gilad Lerman

Byzantine-Robust Federated Learning with Optimal Statistical Rates

Banghua Zhu, Lun Wang, Qi Pang, Shuai Wang, Jiantao Jiao

Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits

Wonyoung Kim, Myunghee Cho Paik, Min-hwan Oh

Multi-armed Bandit Experimental Design: Online Decision-making and Adaptive Inference

David Simchi-Levi, Chonghuan Wang

An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge

Kihyuk Hong, Yuhang Li, Ambuj Tewari

Learning Physics-Informed Neural Networks without Stacked Back-propagation

Di He, Shanda Li, Wenlei Shi, Xiaotian Gao, Jia Zhang

Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets

Hussein Hazimeh, Natalia Ponomareva

Clustering above Exponential Families with Tempered Exponential Measures

Ehsan Amid, Richard Nock, Manfred K. Warmuth

Analysis of Catastrophic Forgetting for Random Orthogonal Transformation Tasks in the Overparameterized Regime

Daniel Goldfarb, Paul Hand

Simulator-Based Inference with WALDO: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems

Luca Masserano, Tommaso Dorigo, Rafael Izbicki, Mikael Kuusela, Ann Lee

Regularization for Shuffled Data Problems via Exponential Family Priors on the Permutation Group

Zhenbang Wang, Emanuel Ben-David, Martin Slawski

Agnostic PAC Learning of k-juntas Using L_2-Polynomial Regression

Mohsen Heidari, Wojciech Szpankowski

On the Limitations of the Elo, Real-World Games are Transitive, not Additive

Quentin Bertrand, Wojciech Marian Czarnecki, Gauthier Gidel

Nearly Optimal Latent State Decoding in Block MDPs

Yassir Jedra, Junghyun Lee, Alexandre Proutiere, Se-Young Yun

Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks

Huishuai Zhang, Da Yu, Yiping Lu, Di He

Sparse Spectral Bayesian Permanental Process with Generalized Kernel

Jeremy Sellier, Petros Dellaportas

Conformal Off-Policy Prediction

Yingying Zhang, Chengchun Shi, Shikai Luo

Improved Rate of First Order Algorithms for Entropic Optimal Transport

Yiling Luo, Yiling Xie, Xiaoming Huo

Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations

Xun Zhu, Yutong Xiong, Ming Wu, Gaozhen Nie, Bin Zhang

Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables

Mengdi Xu, Peide Huang, Yaru Niu, Visak Kumar, Jielin Qiu

EGG-GAE: scalable graph neural networks for tabular data imputation

Lev Telyatnikov, Simone Scardapane

Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE

Young-geun Kim, Ying Liu, Xue-xin Wei

On the Strategyproofness of the Geometric Median

El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Lê-Nguyên Hoang

Learning Constrained Structured Spaces with Application to Multi-Graph Matching

Hedda Cohen Indelman, Tamir Hazan

{PF}$^2$ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization

Jixiang Qing, Henry B. Moss, Tom Dhaene, Ivo Couckuyt

Resolving the Approximability of Offline and Online Non-monotone DR-Submodular Maximization over General Convex Sets

Loay Mualem, Moran Feldman

Nonparametric Indirect Active Learning

Shashank Singh

AUC-based Selective Classification

Andrea Pugnana, Salvatore Ruggieri

Fast Block Coordinate Descent for Non-Convex Group Regularizations

Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai

Deep Neural Networks with Efficient Guaranteed Invariances

Matthias Rath, Alexandru Paul Condurache

Fair learning with Wasserstein barycenters for non-decomposable performance measures

Solenne Gaucher, Nicolas Schreuder, Evgenii Chzhen

Robust Linear Regression for General Feature Distribution

Tom Norman, Nir Weinberger, Kfir Y. Levy

Classification of Adolescents’ Risky Behavior in Instant Messaging Conversations

Jaromı́r Plhák, Ondřej Sotolář, Michaela Lebedı́ková, David Šmahel

AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization

Feihu Huang, Xidong Wu, Zhengmian Hu

Fast Computation of Branching Process Transition Probabilities via ADMM

Achal Awasthi, Jason Xu

“Plus/minus the learning rate”: Easy and Scalable Statistical Inference with SGD

Jerry Chee, Hwanwoo Kim, Panos Toulis

Linear Convergence of Gradient Descent For Finite Width Over-parametrized Linear Networks With General Initialization

Ziqing Xu, Hancheng Min, Salma Tarmoun, Enrique Mallada, René Vidal

A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning

Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, Michael I. Jordan

Knowledge Acquisition for Human-In-The-Loop Image Captioning

Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne Haake

Vector Optimization with Stochastic Bandit Feedback

Cagin Ararat, Cem Tekin

Estimating Total Correlation with Mutual Information Estimators

Ke Bai, Pengyu Cheng, Weituo Hao, Ricardo Henao, Larry Carin

EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model

Yirui Liu, Xinghao Qiao, Liying Wang, Jessica Lam

Mixed Linear Regression via Approximate Message Passing

Nelvin Tan, Ramji Venkataramanan

Mixed-Effect Thompson Sampling

Imad Aouali, Branislav Kveton, Sumeet Katariya

Model-X Sequential Testing for Conditional Independence via Testing by Betting

Shalev Shaer, Gal Maman, Yaniv Romano

Geometric Random Walk Graph Neural Networks via Implicit Layers

Giannis Nikolentzos, Michalis Vazirgiannis

Graph Alignment Kernels using Weisfeiler and Leman Hierarchies

Giannis Nikolentzos, Michalis Vazirgiannis

Variational Inference for Neyman-Scott Processes

Chengkuan Hong, Christian Shelton

One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning

Pedro Cisneros-Velarde, Boxiang Lyu, Sanmi Koyejo, Mladen Kolar

NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge

Xiangyu Sun, Oliver Schulte, Guiliang Liu, Pascal Poupart

Frequentist Uncertainty Quantification in Semi-Structured Neural Networks

Emilio Dorigatti, Benjamin Schubert, Bernd Bischl, David Ruegamer

Energy-Based Models for Functional Data using Path Measure Tilting

Jen Ning Lim, Sebastian Vollmer, Lorenz Wolf, Andrew Duncan

The Ordered Matrix Dirichlet for State-Space Models

Niklas Stoehr, Benjamin J. Radford, Ryan Cotterell, Aaron Schein

Direct Inference of Effect of Treatment (DIET) for a Cookieless World

Shiv Shankar, Ritwik Sinha, Saayan Mitra, Moumita Sinha, Madalina Fiterau

Uni6Dv2: Noise Elimination for 6D Pose Estimation

Mingshan Sun, Ye Zheng, Tianpeng Bao, Jianqiu Chen, Guoqiang Jin

Exact Gradient Computation for Spiking Neural Networks via Forward Propagation

Jane H. Lee, Saeid Haghighatshoar, Amin Karbasi

BlitzMask: Real-Time Instance Segmentation Approach for Mobile Devices

Vitalii Bulygin, Dmytro Mykheievskyi, Kyrylo Kuchynskyi

Neural Laplace Control for Continuous-time Delayed Systems

Samuel Holt, Alihan Hüyük, Zhaozhi Qian, Hao Sun, Mihaela van der Schaar

A Constant-Factor Approximation Algorithm for Reconciliation k-Median

Joachim Spoerhase, Kamyar Khodamoradi, Benedikt Riegel, Bruno Ordozgoiti, Aristides Gionis

A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces

Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa