AISTATS 2023

496 papers

Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data

Batiste Le Bars, Aurélien Bellet, Marc Tommasi, Erick Lavoie, Anne-marie Kermarrec

Deep equilibrium models as estimators for continuous latent variables

Russell Tsuchida, Cheng Soon Ong

Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles

Jung-hun Kim, Se-Young Yun, Minchan Jeong, Junhyun Nam, Jinwoo Shin

Flexible risk design using bi-directional dispersion

Matthew J. Holland

Riemannian Accelerated Gradient Methods via Extrapolation

Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao

On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks

Hongru Yang, Zhangyang Wang

Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes

Felix Jimenez, Matthias Katzfuss

Differentially Private Synthetic Control

Saeyoung Rho, Rachel Cummings, Vishal Misra

Statistical Analysis of Karcher Means for Random Restricted PSD Matrices

Hengchao Chen, Xiang Li, Qiang Sun

Coarse-Grained Smoothness for Reinforcement Learning in Metric Spaces

Omer Gottesman, Kavosh Asadi, Cameron S. Allen, Samuel Lobel, George Konidaris

Positional Encoder Graph Neural Networks for Geographic Data

Konstantin Klemmer, Nathan S. Safir, Daniel B. Neill

Coordinate Ascent for Off-Policy RL with Global Convergence Guarantees

Hsin-En Su, Yen-Ju Chen, Ping-Chun Hsieh, Xi Liu

Testing of Horn Samplers

Ansuman Banerjee, Shayak Chakraborty, Sourav Chakraborty, Kuldeep S. Meel, Uddalok Sarkar

Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity

Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot

Select and Optimize: Learning to solve large-scale TSP instances

Hanni Cheng, Haosi Zheng, Ya Cong, Weihao Jiang, Shiliang Pu

Compress Then Test: Powerful Kernel Testing in Near-linear Time

Carles Domingo-Enrich, Raaz Dwivedi, Lester Mackey

Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework

Runzhe Wan, Lin Ge, Rui Song

Sequential Gradient Descent and Quasi-Newton’s Method for Change-Point Analysis

Xianyang Zhang, Trisha Dawn

Bayesian Hierarchical Models for Counterfactual Estimation

Natraj Raman, Daniele Magazzeni, Sameena Shah

qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization

Raul Astudillo, Zhiyuan Jerry Lin, Eytan Bakshy, Peter Frazier

Can 5th Generation Local Training Methods Support Client Sampling? Yes!

Michał Grudzień, Grigory Malinovsky, Peter Richtarik

Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations

Giannis Nikolentzos, Michail Chatzianastasis, Michalis Vazirgiannis

Improved Robust Algorithms for Learning with Discriminative Feature Feedback

Sivan Sabato

Alternating Projected SGD for Equality-constrained Bilevel Optimization

Quan Xiao, Han Shen, Wotao Yin, Tianyi Chen

Bayesian Optimization with Conformal Prediction Sets

Samuel Stanton, Wesley Maddox, Andrew Gordon Wilson

Optimal and Private Learning from Human Response Data

Duc Nguyen, Anderson Ye Zhang

Characterizing Internal Evasion Attacks in Federated Learning

Taejin Kim, Shubhranshu Singh, Nikhil Madaan, Carlee Joe-Wong

Matching Map Recovery with an Unknown Number of Outliers

Arshak Minasyan, Tigran Galstyan, Sona Hunanyan, Arnak Dalalyan

A New Causal Decomposition Paradigm towards Health Equity

Xinwei Sun, Xiangyu Zheng, Jim Weinstein

Fast Distributed k-Means with a Small Number of Rounds

Tom Hess, Ron Visbord, Sivan Sabato

Adversarial De-confounding in Individualised Treatment Effects Estimation

Vinod K. Chauhan, Soheila Molaei, Marzia Hoque Tania, Anshul Thakur, Tingting Zhu

A Tale of Two Efficient Value Iteration Algorithms for Solving Linear MDPs with Large Action Space

Zhaozhuo Xu, Zhao Song, Anshumali Shrivastava

Global Convergence of Over-parameterized Deep Equilibrium Models

Zenan Ling, Xingyu Xie, Qiuhao Wang, Zongpeng Zhang, Zhouchen Lin

Estimating Conditional Average Treatment Effects with Missing Treatment Information

Milan Kuzmanovic, Tobias Hatt, Stefan Feuerriegel

From Shapley Values to Generalized Additive Models and back

Sebastian Bordt, Ulrike Von Luxburg

Learning in RKHM: a C*-Algebraic Twist for Kernel Machines

Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri

Meta-learning for Robust Anomaly Detection

Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Yasuhiro Fujiwara

Kernel Conditional Moment Constraints for Confounding Robust Inference

Kei Ishikawa, Niao He

Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity

Xun Qian, Hanze Dong, Tong Zhang, Peter Richtarik

Overcoming Prior Misspecification in Online Learning to Rank

Javad Azizi, Ofer Meshi, Masrour Zoghi, Maryam Karimzadehgan

Langevin Diffusion Variational Inference

Tomas Geffner, Justin Domke

Bayesian Structure Scores for Probabilistic Circuits

Yang Yang, Gennaro Gala, Robert Peharz

USIM Gate: UpSampling Module for Segmenting Precise Boundaries concerning Entropy

Kyungsu Lee, Haeyun Lee, Jae Youn Hwang

On the Capacity Limits of Privileged ERM

Michal Sharoni, Sivan Sabato

Semi-Verified PAC Learning from the Crowd

Shiwei Zeng, Jie Shen

Learning k-qubit Quantum Operators via Pauli Decomposition

Mohsen Heidari, Wojciech Szpankowski

A principled framework for the design and analysis of token algorithms

Hadrien Hendrikx

Sample Complexity of Kernel-Based Q-Learning

Sing-Yuan Yeh, Fu-Chieh Chang, Chang-Wei Yueh, Pei-Yuan Wu, Alberto Bernacchia

Adaptive Cholesky Gaussian Processes

Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Muñoz, Wouter Boomsma, Jes Frellsen

Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement Learning

Ruitu Xu, Yifei Min, Tianhao Wang, Michael I. Jordan, Zhaoran Wang

A Case of Exponential Convergence Rates for SVM

Vivien Cabannnes, Stefano Vigogna

Online Defense Strategies for Reinforcement Learning Against Adaptive Reward Poisoning

Andi Nika, Adish Singla, Goran Radanovic

Gradient-Informed Neural Network Statistical Robustness Estimation

Karim TIT, Teddy Furon, Mathias ROUSSET

Barlow Graph Auto-Encoder for Unsupervised Network Embedding

Rayyan Ahmad Khan, Martin Kleinsteuber

Bayesian Variable Selection in a Million Dimensions

Martin Jankowiak

Scalable marked point processes for exchangeable and non-exchangeable event sequences

Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas

Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods

Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou

Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision

Jieyu Zhang, Linxin Song, Alex Ratner

An Online and Unified Algorithm for Projection Matrix Vector Multiplication with Application to Empirical Risk Minimization

Lianke Qin, Zhao Song, Lichen Zhang, Danyang Zhuo

Acceleration of Frank-Wolfe Algorithms with Open-Loop Step-Sizes

Elias Wirth, Thomas Kerdreux, Sebastian Pokutta

Entropic Risk Optimization in Discounted MDPs

Jia Lin Hau, Marek Petrik, Mohammad Ghavamzadeh

PAC Learning of Halfspaces with Malicious Noise in Nearly Linear Time

Jie Shen

Meta-Uncertainty in Bayesian Model Comparison

Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner

Improved Generalization Bound and Learning of Sparsity Patterns for Data-Driven Low-Rank Approximation

Shinsaku Sakaue, Taihei Oki

Using Sliced Mutual Information to Study Memorization and Generalization in Deep Neural Networks

Shelvia Wongso, Rohan Ghosh, Mehul Motani

The Power of Recursion in Graph Neural Networks for Counting Substructures

Behrooz Tahmasebi, Derek Lim, Stefanie Jegelka

Fitting low-rank models on egocentrically sampled partial networks

Ga Ming Angus Chan, Tianxi Li

Implicit Graphon Neural Representation

Xinyue Xia, Gal Mishne, Yusu Wang

Who Should Predict? Exact Algorithms For Learning to Defer to Humans

Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das

Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation

Garud Iyengar, Henry Lam, Tianyu Wang

Huber-robust confidence sequences

Hongjian Wang, Aaditya Ramdas

Scalable Bicriteria Algorithms for Non-Monotone Submodular Cover

Victoria Crawford

Origins of Low-Dimensional Adversarial Perturbations

Elvis DOHMATOB, Chuan Guo, Morgane Goibert

Don’t be fooled: label leakage in explanation methods and the importance of their quantitative evaluation

Neil Jethani, Adriel Saporta, Rajesh Ranganath

Rethinking Initialization of the Sinkhorn Algorithm

James Thornton, Marco Cuturi

Fix-A-Step: Semi-supervised Learning From Uncurated Unlabeled Data

Zhe Huang, Mary-Joy Sidhom, Benjamin Wessler, Michael C. Hughes

Mode-Seeking Divergences: Theory and Applications to GANs

Cheuk Ting Li, Farzan Farnia

Do Bayesian Neural Networks Need To Be Fully Stochastic?

Mrinank Sharma, Sebastian Farquhar, Eric Nalisnick, Tom Rainforth

Indeterminacy in Generative Models: Characterization and Strong Identifiability

Quanhan Xi, Benjamin Bloem-Reddy

Data Banzhaf: A Robust Data Valuation Framework for Machine Learning

Jiachen T. Wang, Ruoxi Jia

Federated Learning under Distributed Concept Drift

Ellango Jothimurugesan, Kevin Hsieh, Jianyu Wang, Gauri Joshi, Phillip B. Gibbons

The Schrödinger Bridge between Gaussian Measures has a Closed Form

Charlotte Bunne, Ya-Ping Hsieh, Marco Cuturi, Andreas Krause

Implications of sparsity and high triangle density for graph representation learning

Hannah Sansford, Alexander Modell, Nick Whiteley, Patrick Rubin-Delanchy

Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation

Henry B. Moss, Sebastian W. Ober, Victor Picheny

Particle algorithms for maximum likelihood training of latent variable models

Juan Kuntz, Jen Ning Lim, Adam M. Johansen

Safe Sequential Testing and Effect Estimation in Stratified Count Data

Rosanne Turner, Peter Grünwald

An Efficient and Continuous Voronoi Density Estimator

Giovanni Luca Marchetti, Vladislav Polianskii, Anastasiia Varava, Florian T. Pokorny, Danica Kragic

A Tale of Sampling and Estimation in Discounted Reinforcement Learning

Alberto Maria Metelli, Mirco Mutti, Marcello Restelli

Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy

Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang

Noisy Low-rank Matrix Optimization: Geometry of Local Minima and Convergence Rate

Ziye Ma, Somayeh Sojoudi

Error Estimation for Random Fourier Features

Junwen Yao, N. Benjamin Erichson, Miles E. Lopes

Distance-to-Set Priors and Constrained Bayesian Inference

Rick Presman, Jason Xu

Multilevel Bayesian Quadrature

Kaiyu Li, Daniel Giles, Toni Karvonen, Serge Guillas, François-Xavier Briol

Discovering Many Diverse Solutions with Bayesian Optimization

Natalie Maus, Kaiwen Wu, David Eriksson, Jacob Gardner

BaCaDI: Bayesian Causal Discovery with Unknown Interventions

Alexander Hägele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath, Bernhard Schölkopf

Blessing of Class Diversity in Pre-training

Yulai Zhao, Jianshu Chen, Simon Du