Simple and near-optimal algorithms for hidden stratification and
multi-group learning

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Simple and near-optimal algorithms for hidden stratification and multi-group learning

International Conference on Machine Learning (ICML), 2021

22 December 2021

Abdoreza Asadpour

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Papers citing "Simple and near-optimal algorithms for hidden stratification and multi-group learning"

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Title
Sample-Adaptivity Tradeoff in On-Demand Sampling Nika Haghtalab Omar Montasser Mingda Qiao 120 0 0 19 Nov 2025
Panprediction: Optimal Predictions for Any Downstream Task and Loss Sivaraman Balakrishnan Nika Haghtalab Daniel Hsu Brian Lee Eric Zhao 60 0 0 31 Oct 2025
Group-wise oracle-efficient algorithms for online multi-group learningNeural Information Processing Systems (NeurIPS), 2024 Samuel Deng Daniel J. Hsu Jingwen Liu 268 2 0 07 Jun 2024
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning Ira Globus-Harris Declan Harrison Michael Kearns Pietro Perona Aaron Roth FedML 124 2 0 16 Feb 2024
Optimal Multi-Distribution LearningJournal of the ACM (JACM), 2023 Zihan Zhang Wenhao Zhan Yuxin Chen Simon S. Du Jason D. Lee 324 15 0 08 Dec 2023
The sample complexity of multi-distribution learning Binghui Peng 309 11 0 07 Dec 2023
Agnostic Multi-Group Active LearningNeural Information Processing Systems (NeurIPS), 2023 Nick Rittler Kamalika Chaudhuri 125 3 0 02 Jun 2023
Group conditional validity via multi-group learning Samuel Deng Navid Ardeshir Daniel J. Hsu 194 1 0 07 Mar 2023
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play J. Liu Krishnamurthy Dvijotham Jihyeon Janel Lee Quan Yuan Martin Strobel Balaji Lakshminarayanan Deepak Ramachandran 178 5 0 11 Feb 2023
Comparative Learning: A Sample Complexity Theory for Two Hypothesis ClassesInformation Technology Convergence and Services (ITCS), 2022 Lunjia Hu Charlotte Peale 133 8 0 16 Nov 2022
On-Demand Sampling: Learning Optimally from Multiple DistributionsNeural Information Processing Systems (NeurIPS), 2022 Nika Haghtalab Michael I. Jordan Eric Zhao FedML 394 43 0 22 Oct 2022
Low-Degree MulticalibrationAnnual Conference Computational Learning Theory (COLT), 2022 Parikshit Gopalan Michael P. Kim M. Singhal Shengjia Zhao FaML UQCV 224 50 0 02 Mar 2022