Papers
Adaptive importance sampling for heavy-tailed distributions via $α$-divergence minimization
Thomas Guilmeau, Nicola Branchini, Emilie Chouzenoux et al.
Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification
Marzi Heidari, Abdullah Alchihabi, Qing En et al.
Adaptivity of Diffusion Models to Manifold Structures
Rong Tang, Yun Yang
A Doubly Robust Approach to Sparse Reinforcement Learning
Wonyoung Kim, Garud Iyengar, Assaf Zeevi
A General Algorithm for Solving Rank-one Matrix Sensing
Lianke Qin, Zhao Song, Ruizhe Zhang
A General Theoretical Paradigm to Understand Learning from Human Preferences
Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot et al.
Agnostic Multi-Robust Learning using ERM
Saba Ahmadi, Avrim Blum, Omar Montasser et al.
A Greedy Approximation for k-Determinantal Point Processes
Julia Grosse, Rahel Fischer, Roman Garnett et al.
ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data
Maria C. Novitasari, Johannes Quaas, Miguel Rodrigues
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
Mathieu Dagréou, Thomas Moreau, Samuel Vaiter et al.
Analysis of Kernel Mirror Prox for Measure Optimization
Pavel Dvurechensky, Jia-Jie Zhu
Analysis of Privacy Leakage in Federated Large Language Models
Minh Vu, Truc Nguyen, Tre’ Jeter et al.
Analysis of Using Sigmoid Loss for Contrastive Learning
Chungpa Lee, Joonhwan Chang, Jy-yong Sohn
Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions
Zulqarnain Q. Khan, Davin Hill, Aria Masoomi et al.
An Analytic Solution to Covariance Propagation in Neural Networks
Oren Wright, Yorie Nakahira, José M. F. Moura
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization
Lesi Chen, Haishan Ye, Luo Luo
A Neural Architecture Predictor based on GNN-Enhanced Transformer
Xunzhi Xiang, Kun Jing, Jungang Xu
An Impossibility Theorem for Node Embedding
T. Mitchell Roddenberry, Yu Zhu, Santiago Segarra
An Improved Algorithm for Learning Drifting Discrete Distributions
Alessio Mazzetto
An Online Bootstrap for Time Series
Nicolai Palm, Thomas Nagler
Any-dimensional equivariant neural networks
Eitan Levin, Mateo Diaz
Anytime-Constrained Reinforcement Learning
Jeremy McMahan, Xiaojin Zhu
Approximate Bayesian Class-Conditional Models under Continuous Representation Shift
Thomas L. Lee, Amos Storkey
Approximate Control for Continuous-Time POMDPs
Yannick Eich, Bastian Alt, Heinz Koeppl