Multi-distribution learning (MDL), which seeks to learn a shared model that minimizes the worst-case risk across $k$ distinct data distributions, has emerged as a unified framework in response to the evolving demand for robustness, fairness, multi-group collaboration, etc. Achieving data-efficient MDL necessitates adaptive sampling, also called on-demand sampling, throughout the learning process. However, there exist substantial gaps between the state-of-the-art upper and lower bounds on the optimal sample complexity. Focusing on a hypothesis class of Vapnik-Chervonenkis (VC) dimension $d$, we propose a novel algorithm that yields an $varepsilon$-optimal randomized hypothesis with a sample complexity on the order of $(d+k)/\varepsilon^2$ (modulo some logarithmic factor), matching the best-known lower bound. Our algorithmic ideas and theory have been further extended to accommodate Rademacher classes. The proposed algorithms are oracle-efficient, which access the hypothesis class solely through an empirical risk minimization oracle. Additionally, we establish the necessity of randomization, unveiling a large sample size barrier when only deterministic hypotheses are permitted. These findings successfully resolve three open problems presented in COLT 2023 (i.e., Awasthi et al., (2023, Problem 1, 3 and 4)).

翻译：多分布学习（MDL）旨在学习一个共享模型，以最小化$k$个不同数据分布上的最坏情况风险，已成为应对鲁棒性、公平性、多组协作等需求不断演变的统一框架。实现数据高效的MDL需要在整个学习过程中采用自适应采样（也称为按需采样）。然而，关于最优样本复杂度的现有上界与下界之间存在显著差距。针对Vapnik-Chervonenkis (VC)维度为$d$的假设类，我们提出了一种新算法，该算法能以$(d+k)/\varepsilon^2$量级的样本复杂度（忽略对数因子）生成$\varepsilon$最优的随机化假设，与已知最佳下界相匹配。我们的算法思想与理论已进一步扩展至Rademacher类。所提算法具有预言机高效性，仅通过经验风险最小化预言机访问假设类。此外，我们揭示了随机化的必要性，发现仅允许确定性假设时存在大样本量障碍。这些发现成功解决了COLT 2023中提出的三个开放问题（即Awasthi等人，2023，问题1、3和4）。