AISTATS

Publications

2024

Neharika Jali, Guannan Qu, Weina Wang, Gauri Joshi
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems. AISTATS (2024)
Jhanvi Garg, Xianyang Zhang, Quan Zhou
Soft-constrained Schrödinger Bridge: a Stochastic Control Approach. AISTATS (2024)
Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain, Branislav Kveton, Ge Liu
Pessimistic Off-Policy Multi-Objective Optimization. AISTATS (2024)
Maheed H. Ahmed, Mahsa Ghasemi
Privacy-Preserving Decentralized Actor-Critic for Cooperative Multi-Agent Reinforcement Learning. AISTATS (2024)
Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar
DAGnosis: Localized Identification of Data Inconsistencies using Structures. AISTATS (2024)
Samson J. Koelle, Hanyu Zhang, Octavian-Vlad Murad, Marina Meila
Consistency of Dictionary-Based Manifold Learning. AISTATS (2024)
Zhao Song, Junze Yin, Lichen Zhang, Ruizhe Zhang
Fast Dynamic Sampling for Determinantal Point Processes. AISTATS (2024)
Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier
Identifying Copeland Winners in Dueling Bandits with Indifferences. AISTATS (2024)
Chung-En Tsai, Hao-Chung Cheng, Yen-Huan Li
Fast Minimization of Expected Logarithmic Loss via Stochastic Dual Averaging. AISTATS (2024)
Chendi Qian, Didier Chételat, Christopher Morris
Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems. AISTATS (2024)
Michael Rizvi-Martel, Maude Lizaire, Clara Lacroce, Guillaume Rabusseau
Simulating weighted automata over sequences and trees with transformers. AISTATS (2024)
Soo Min Kwon, Zekai Zhang, Dogyoon Song, Laura Balzano, Qing Qu
Efficient Low-Dimensional Compression of Overparameterized Models. AISTATS (2024)
Chris Cundy, Rishi Desai, Stefano Ermon
Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients. AISTATS (2024)
Ethan B. Andrew, David Westhead, Luisa Cutillo
GmGM: a fast multi-axis Gaussian graphical model. AISTATS (2024)
Ayman Chaouki, Jesse Read, Albert Bifet
Online Learning of Decision Trees with Thompson Sampling. AISTATS (2024)
Zitong Ma, Wenbo Zhao, Zhe Yang
Directed Hypergraph Representation Learning for Link Prediction. AISTATS (2024)
Daniel Dold, David Rügamer, Beate Sick, Oliver Dürr
Bayesian Semi-structured Subspace Inference. AISTATS (2024)
Yingcong Li, Yixiao Huang, Muhammed Emrullah Ildiz, Ankit Singh Rawat, Samet Oymak
Mechanics of Next Token Prediction with Self-Attention. AISTATS (2024)
Jimmy Hickey, Ricardo Henao, Daniel Wojdyla, Michael J. Pencina, Matthew Engelhard
Adaptive Discretization for Event PredicTion (ADEPT). AISTATS (2024)
Yuya Yoshikawa, Tomoharu Iwata
Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizers. AISTATS (2024)
Siqi Zhang, Yifan Hu, Liang Zhang, Niao He
Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization. AISTATS (2024)
Cai Zhou, Rose Yu, Yusu Wang
On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers. AISTATS (2024)
Dongxia Wu, Tsuyoshi Idé, Georgios Kollias, Jirí Navrátil, Aurélie C. Lozano, Naoki Abe, Yi-An Ma, Rose Yu
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes. AISTATS (2024)
Bianca Marin Moreno, Margaux Brégère, Pierre Gaillard, Nadia Oudjane
Efficient Model-Based Concave Utility Reinforcement Learning through Greedy Mirror Descent. AISTATS (2024)
Tom Sander, Maxime Sylvestre, Alain Durmus
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training. AISTATS (2024)
Jiaqi Zhang, Kirankumar Shiragur, Caroline Uhler
Membership Testing in Markov Equivalence Classes via Independence Queries. AISTATS (2024)
Nikita Puchkin, Eduard Gorbunov, Nikolay Kutuzov, Alexander V. Gasnikov
Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems. AISTATS (2024)
Francesco Bacchiocchi, Gianmarco Genalti, Davide Maran, Marco Mussi, Marcello Restelli, Nicola Gatti, Alberto Maria Metelli
Autoregressive Bandits. AISTATS (2024)
Velibor Bojkovic, Srinivas Anumasa, Giulia De Masi, Bin Gu, Huan Xiong
Data Driven Threshold and Potential Initialization for Spiking Neural Networks. AISTATS (2024)
Lucas Cosier, Rares Iordan, Sicelukwanda N. T. Zwane, Giovanni Franzese, James T. Wilson, Marc Peter Deisenroth, Alexander Terenin, Yasemin Bekiroglu
A Unifying Variational Framework for Gaussian Process Motion Planning. AISTATS (2024)
Kevin Li, Max Balakirsky, Simon Mak
Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regression. AISTATS (2024)
Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov
Efficient Conformal Prediction under Data Heterogeneity. AISTATS (2024)
Yun-Peng Li, Hans-Andrea Loeliger
Backward Filtering Forward Deciding in Linear Non-Gaussian State Space Models. AISTATS (2024)
Rohan Deb, Aadirupa Saha, Arindam Banerjee
Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources. AISTATS (2024)
Junze Deng, Yuan Cheng, Shaofeng Zou, Yingbin Liang
Sample Complexity Characterization for Linear Contextual MDPs. AISTATS (2024)
Rustem Islamov, Mher Safaryan, Dan Alistarh
AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms. AISTATS (2024)
Naitong Chen, Trevor Campbell
Coreset Markov chain Monte Carlo. AISTATS (2024)
Boris Flach, Dmitrij Schlesinger, Alexander Shekhovtsov
Symmetric Equilibrium Learning of VAEs. AISTATS (2024)
Yu Inatsu, Shion Takeno, Hiroyuki Hanada, Kazuki Iwata, Ichiro Takeuchi
Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty. AISTATS (2024)
Pavan Karjol, Rohan Kashyap, Aditya Gopalan, A. P. Prathosh
A Unified Framework for Discovering Discrete Symmetries. AISTATS (2024)
Hao Liang, Zhiquan Luo
Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz Dynamic Risk Measures. AISTATS (2024)
Yinuo Ren, Chao Ma, Lexing Ying
Understanding the Generalization Benefits of Late Learning Rate Decay. AISTATS (2024)
Lesi Chen, Haishan Ye, Luo Luo
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. AISTATS (2024)
Ryan Thompson, Edwin V. Bonilla, Robert Kohn
Contextual Directed Acyclic Graphs. AISTATS (2024)
Patrick Kolpaczki, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier
SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification. AISTATS (2024)
Yu Yang, Eric Gan, Gintare Karolina Dziugaite, Baharan Mirzasoleiman
Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias. AISTATS (2024)
Tolga Dimlioglu, Anna Choromanska
GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models. AISTATS (2024)
Alexandra Maria Hotti, Lennart Alexander Van der Goten, Jens Lagergren
Benefits of Non-Linear Scale Parameterizations in Black Box Variational Inference through Smoothness Results and Gradient Variance Bounds. AISTATS (2024)
Mohan Wu, Martin Lysy
Data-Adaptive Probabilistic Likelihood Approximation for Ordinary Differential Equations. AISTATS (2024)

volume 238, 2024

International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain. AISTATS 238 (2024)