On-Demand Sampling: Learning Optimally from Multiple DistributionsAuthors are ordered alphabetically. Correspondence to eric.zh@berkeley.edu.
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On-Demand Sampling: Learning Optimally from Multiple DistributionsAuthors are ordered alphabetically. Correspondence to eric.zh@berkeley.edu. On-Demand Sampling: Learning Optimally from Multiple Distributions † † thanks: Authors are ordered alphabetically. Correspondence to eric.zh@berkeley.edu . Nika Haghtalab Michael I. Jordan Eric Zhao Abstract Social and real-world considerations such as robustness, fairness, social welfare and multi-agent tradeoffs have given rise to multi-distribution learning paradigms, such as collaborative [ 9 ] , group distributionally robust [ 50 ] , and fair federa
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