AISTATS 2024

547 papers

A Unifying Variational Framework for Gaussian Process Motion Planning

Lucas C. Cosier, Rares Iordan, Sicelukwanda N. T. Zwane, Giovanni Franzese, James T. Wilson

Intrinsic Gaussian Vector Fields on Manifolds

Daniel Robert-Nicoud, Andreas Krause, Viacheslav Borovitskiy

Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels

Raphaël Carpintero Perez, Sébastien Da Veiga, Josselin Garnier, Brian Staber

Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees

Alexia Jolicoeur-Martineau, Kilian Fatras, Tal Kachman

Simulation-Free Schrödinger Bridges via Score and Flow Matching

Alexander Y. Tong, Nikolay Malkin, Kilian Fatras, Lazar Atanackovic, Yanlei Zhang

Fair Soft Clustering

Rune D. Kjærsgaard, Pekka Parviainen, Saket Saurabh, Madhumita Kundu, Line Clemmensen

Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models

Frederiek Wesel, Kim Batselier

Risk Seeking Bayesian Optimization under Uncertainty for Obtaining Extremum

Shogo Iwazaki, Tomohiko Tanabe, Mitsuru Irie, Shion Takeno, Yu Inatsu

Corruption-Robust Offline Two-Player Zero-Sum Markov Games

Andi Nika, Debmalya Mandal, Adish Singla, Goran Radanovic

Scalable Learning of Item Response Theory Models

Susanne Frick, Amer Krivosija, Alexander Munteanu

Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit

Shintaro Nakamura, Masashi Sugiyama

Best-of-Both-Worlds Algorithms for Linear Contextual Bandits

Yuko Kuroki, Alberto Rumi, Taira Tsuchiya, Fabio Vitale, Nicolò Cesa-bianchi

Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates

Ahmad Rammal, Kaja Gruntkowska, Nikita Fedin, Eduard Gorbunov, Peter Richtarik

Uncertainty Matters: Stable Conclusions under Unstable Assessment of Fairness Results

Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano, Novi Quadrianto

Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization

Alejandro D. de la Concha Duarte, Nicolas Vayatis, Argyris Kalogeratos

Adaptive Experiment Design with Synthetic Controls

Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar

Efficient Graph Laplacian Estimation by Proximal Newton

Yakov Medvedovsky, Eran Treister, Tirza S Routtenberg

Learning Sampling Policy to Achieve Fewer Queries for Zeroth-Order Optimization

Zhou Zhai, Wanli Shi, Heng Huang, Yi Chang, Bin Gu

Fast and Adversarial Robust Kernelized SDU Learning

Yajing Fan, Wanli Shi, Yi Chang, Bin Gu

Robust variance-regularized risk minimization with concomitant scaling

Matthew J. Holland

Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models

Soheun Yi, Sanghack Lee

When No-Rejection Learning is Consistent for Regression with Rejection

Xiaocheng Li, Shang Liu, Chunlin Sun, Hanzhao Wang

Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles

Fan Yang, Pierre Le Bodic, Michael Kamp, Mario Boley

Scalable Algorithms for Individual Preference Stable Clustering

Ron Mosenzon, Ali Vakilian

Information-theoretic Analysis of Bayesian Test Data Sensitivity

Futoshi Futami, Tomoharu Iwata

Effect of Ambient-Intrinsic Dimension Gap on Adversarial Vulnerability

Rajdeep Haldar, Yue Xing, Qifan Song

Submodular Minimax Optimization: Finding Effective Sets

Loay Raed Mualem, Ethan R Elenberg, Moran Feldman, Amin Karbasi

Quantifying Uncertainty in Natural Language Explanations of Large Language Models

Sree Harsha Tanneru, Chirag Agarwal, Himabindu Lakkaraju

Distributionally Robust Quickest Change Detection using Wasserstein Uncertainty Sets

Liyan Xie, Yuchen Liang, Venugopal V. Veeravalli

Towards Achieving Sub-linear Regret and Hard Constraint Violation in Model-free RL

Arnob Ghosh, Xingyu Zhou, Ness Shroff

Hidden yet quantifiable: A lower bound for confounding strength using randomized trials

Piersilvio De Bartolomeis, Javier Abad Martinez, Konstantin Donhauser, Fanny Yang

Unified Transfer Learning in High-Dimensional Linear Regression

Shuo Shuo Liu

Fairness in Submodular Maximization over a Matroid Constraint

Marwa El Halabi, Jakub Tarnawski, Ashkan Norouzi-Fard, Thuy-Duong Vuong

Data-Adaptive Probabilistic Likelihood Approximation for Ordinary Differential Equations

Mohan Wu, Martin Lysy

Efficient Low-Dimensional Compression of Overparameterized Models

Soo Min Kwon, Zekai Zhang, Dogyoon Song, Laura Balzano, Qing Qu

Data-Efficient Contrastive Language-Image Pretraining: Prioritizing Data Quality over Quantity

Siddharth Joshi, Arnav Jain, Ali Payani, Baharan Mirzasoleiman

Robust Sparse Voting

Youssef Allouah, Rachid Guerraoui, Lê-Nguyên Hoang, Oscar Villemaud

Graph Machine Learning through the Lens of Bilevel Optimization

Amber Yijia Zheng, Tong He, Yixuan Qiu, Minjie Wang, David Wipf

Multi-Dimensional Hyena for Spatial Inductive Bias

Itamar Zimerman, Lior Wolf

MINTY: Rule-based models that minimize the need for imputing features with missing values

Lena Stempfle, Fredrik Johansson

Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization

Zhenzhang Ye, Gabriel Peyré, Daniel Cremers, Pierre Ablin

DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data

Taehyo Kim, Hai Shu, Qiran Jia, Mony de Leon

Autoregressive Bandits

Francesco Bacchiocchi, Gianmarco Genalti, Davide Maran, Marco Mussi, Marcello Restelli

Learning-Based Algorithms for Graph Searching Problems

Adela F. DePavia, Erasmo Tani, Ali Vakilian

Monitoring machine learning-based risk prediction algorithms in the presence of performativity

Jean Feng, Alexej Gossmann, Gene A Pennello, Nicholas Petrick, Berkman Sahiner

Categorical Generative Model Evaluation via Synthetic Distribution Coarsening

Florence Regol, Mark Coates

A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models

Zhongliang Guo, Weiye Li, Yifei Qian, Ognjen Arandjelovic, Lei Fang

BOBA: Byzantine-Robust Federated Learning with Label Skewness

Wenxuan Bao, Jun Wu, Jingrui He

Adaptive Quasi-Newton and Anderson Acceleration Framework with Explicit Global (Accelerated) Convergence Rates

Damien Scieur

Unsupervised Novelty Detection in Pretrained Representation Space with Locally Adapted Likelihood Ratio

Amirhossein Ahmadian, Yifan Ding, Gabriel Eilertsen, Fredrik Lindsten

Multi-Domain Causal Representation Learning via Weak Distributional Invariances

Kartik Ahuja, Amin Mansouri, Yixin Wang

Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems

Nikita Puchkin, Eduard Gorbunov, Nickolay Kutuzov, Alexander Gasnikov

Making Better Use of Unlabelled Data in Bayesian Active Learning

Freddie Bickford Smith, Adam Foster, Tom Rainforth

Sharp error bounds for imbalanced classification: how many examples in the minority class?

Anass Aghbalou, Anne Sabourin, François Portier

Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support

Tim Reichelt, Luke Ong, Tom Rainforth

Riemannian Laplace Approximation with the Fisher Metric

Hanlin Yu, Marcelo Hartmann, Bernardo Williams Moreno Sanchez, Mark Girolami, Arto Klami

Asymptotic Characterisation of the Performance of Robust Linear Regression in the Presence of Outliers

Matteo Vilucchio, Emanuele Troiani, Vittorio Erba, Florent Krzakala

Recovery Guarantees for Distributed-OMP

Chen Amiraz, Robert Krauthgamer, Boaz Nadler

A Unified Framework for Discovering Discrete Symmetries

Pavan Karjol, Rohan Kashyap, Aditya Gopalan, A. P. Prathosh

Lower-level Duality Based Reformulation and Majorization Minimization Algorithm for Hyperparameter Optimization

He Chen, Haochen Xu, Rujun Jiang, Anthony Man-Cho So

The Solution Path of SLOPE

Xavier Dupuis, Patrick Tardivel

Oracle-Efficient Pessimism: Offline Policy Optimization In Contextual Bandits

Lequn Wang, Akshay Krishnamurthy, Alex Slivkins

A General Algorithm for Solving Rank-one Matrix Sensing

Lianke Qin, Zhao Song, Ruizhe Zhang

E(3)-Equivariant Mesh Neural Networks

Thuan Anh Trang, Nhat Khang Ngo, Daniel T. Levy, Thieu Ngoc Vo, Siamak Ravanbakhsh

Reparameterized Variational Rejection Sampling

Martin Jankowiak, Du Phan

Proximal Causal Inference for Synthetic Control with Surrogates

Jizhou Liu, Eric Tchetgen Tchetgen, Carlos Varjão

Exploration via linearly perturbed loss minimisation

David Janz, Shuai Liu, Alex Ayoub, Csaba Szepesvári

Fusing Individualized Treatment Rules Using Secondary Outcomes

Daiqi Gao, Yuanjia Wang, Donglin Zeng

TransFusion: Covariate-Shift Robust Transfer Learning for High-Dimensional Regression

Zelin He, Ying Sun, Runze Li

Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization

Siqi Zhang, Yifan Hu, Liang Zhang, Niao He

Mechanics of Next Token Prediction with Self-Attention

Yingcong Li, Yixiao Huang, Muhammed E. Ildiz, Ankit Singh Rawat, Samet Oymak

Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters

Zhenyu Sun, Xiaochun Niu, Ermin Wei

Theory-guided Message Passing Neural Network for Probabilistic Inference

Zijun Cui, Hanjing Wang, Tian Gao, Kartik Talamadupula, Qiang Ji

Directional Optimism for Safe Linear Bandits

Spencer Hutchinson, Berkay Turan, Mahnoosh Alizadeh

AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms

Rustem Islamov, Mher Safaryan, Dan Alistarh

LEDetection: A Simple Framework for Semi-Supervised Few-Shot Object Detection

Phi Vu Tran

Federated Linear Contextual Bandits with Heterogeneous Clients

Ethan Blaser, Chuanhao Li, Hongning Wang

Deep anytime-valid hypothesis testing

Teodora Pandeva, Patrick Forré, Aaditya Ramdas, Shubhanshu Shekhar

Compression with Exact Error Distribution for Federated Learning

Mahmoud Hegazy, Rémi Leluc, Cheuk Ting Li, Aymeric Dieuleveut

Clustering Items From Adaptively Collected Inconsistent Feedback

Shubham Gupta, Peter W J Staar, Christian de Sainte Marie

Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias

Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko

Structured Transforms Across Spaces with Cost-Regularized Optimal Transport

Othmane Sebbouh, Marco Cuturi, Gabriel Peyré

On Ranking-based Tests of Independence

Myrto Limnios, Stéphan Clémençon

Graph Partitioning with a Move Budget

Mina Dalirrooyfard, Elaheh Fata, Majid Behbahani, Yuriy Nevmyvaka

Prior-dependent analysis of posterior sampling reinforcement learning with function approximation

Yingru Li, Zhiquan Luo

The Risks of Recourse in Binary Classification

Hidde Fokkema, Damien Garreau, Tim van Erven

Robust Offline Reinforcement Learning with Heavy-Tailed Rewards

Jin Zhu, Runzhe Wan, Zhengling Qi, Shikai Luo, Chengchun Shi

VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates

Guillaume Braun, Masashi Sugiyama

Meta Learning in Bandits within shared affine Subspaces

Steven Bilaj, Sofien Dhouib, Setareh Maghsudi

Efficient Quantum Agnostic Improper Learning of Decision Trees

Sagnik Chatterjee, Tharrmashastha SAPV, Debajyoti Bera

Looping in the Human: Collaborative and Explainable Bayesian Optimization

Masaki Adachi, Brady Planden, David Howey, Michael A. Osborne, Sebastian Orbell

Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach

Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Xingchen Wan, Vu Nguyen

Adaptive Compression in Federated Learning via Side Information

Berivan Isik, Francesco Pase, Deniz Gündüz, Sanmi Koyejo, Tsachy Weissman

Delegating Data Collection in Decentralized Machine Learning

Nivasini Ananthakrishnan, Stephen Bates, Michael Jordan, Nika Haghtalab

Minimax optimal density estimation using a shallow generative model with a one-dimensional latent variable

Hyeok Kyu Kwon, Minwoo Chae

Online Distribution Learning with Local Privacy Constraints

Jin Sima, Changlong Wu, Olgica Milenkovic, Wojciech Szpankowski

The Galerkin method beats Graph-Based Approaches for Spectral Algorithms

Vivien A. Cabannes, Francis Bach

Testing Generated Distributions in GANs to Penalize Mode Collapse

Yanxiang Gong, Zhiwei Xie, Mei Xie, Xin Ma

Faster Convergence with MultiWay Preferences

Aadirupa Saha, Vitaly Feldman, Yishay Mansour, Tomer Koren

P-tensors: a General Framework for Higher Order Message Passing in Subgraph Neural Networks

Andrew R. Hands, Tianyi Sun, Risi Kondor