On-Demand Sampling: Learning Optimally from Multiple Distributions

22 October 2022

Papers citing "On-Demand Sampling: Learning Optimally from Multiple Distributions"

7 / 7 papers shown

Title
Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity Quan Nguyen Nishant A. Mehta Cristóbal Guzmán 34 0 0 01 Oct 2024
An Axiomatic Approach to Loss Aggregation and an Adapted Aggregating Algorithm Armando J. Cabrera Pacheco Rabanus Derr Robert C. Williamson 30 0 0 04 Jun 2024
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication Kumar Kshitij Patel Margalit Glasgow Ali Zindari Lingxiao Wang Sebastian U. Stich Ziheng Cheng Nirmit Joshi Nathan Srebro 26 6 0 19 May 2024
A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning Nika Haghtalab Michael I. Jordan Eric Zhao 27 8 0 21 Feb 2023
Simple and near-optimal algorithms for hidden stratification and multi-group learning Abdoreza Asadpour Daniel J. Hsu 84 19 0 22 Dec 2021
Adversarial Laws of Large Numbers and Optimal Regret in Online Classification N. Alon Omri Ben-Eliezer Y. Dagan Shay Moran M. Naor E. Yogev 63 45 0 22 Jan 2021
An Investigation of Why Overparameterization Exacerbates Spurious Correlations Shiori Sagawa Aditi Raghunathan Pang Wei Koh Percy Liang 131 368 0 09 May 2020