在分组可实现设定下，多组学习的样本效率显著提升。
Group-realizable multi-group learning by minimizing empirical risk
- 通过最小化经验风险优化分组可实现概念类
- 即使分组族无限，只要VC维有限，样本复杂度就降低
- 适合关注公平性与高效学习的研究者
多组学习的样本复杂度在分组可实现设定下优于泛化设定，即使分组族为无限集，只要其VC维有限，该优势依然成立。这一改进通过在分组可实现概念类上进行经验风险最小化实现，该概念类本身可能具有无限VC维。然而，该方法被证明在计算上不可行，因此提出了基于非正当学习的替代方案。
原文摘要 · Abstract (English)
The sample complexity of multi-group learning is shown to improve in the group-realizable setting over the agnostic setting, even when the family of groups is infinite so long as it has finite VC dimension. The improved sample complexity is obtained by empirical risk minimization over the class of group-realizable concepts, which itself could have infinite VC dimension. Implementing this approach is also shown to be computationally intractable, and an alternative approach is suggested based on improper learning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。