Optimal On-Demand Sampling for Learning from Multiple Distributions

The core message of this paper is to establish the optimal sample complexity of multi-distribution learning paradigms, such as collaborative learning, group distributionally robust optimization, and agnostic federated learning. The authors show that their algorithms can achieve this optimal sample complexity by learning to sample from data distributions on demand.