Boosting is a key method in statistical learning, allowing for converting weak learners into strong ones. While well studied in the realizable case, the statistical properties of weak-to-strong learning remain less understood in the agnostic setting, where there are no assumptions on the distribution of the labels. In this work, we propose a new agnostic boosting algorithm with substantially improved sample complexity compared to prior works under very general assumptions. Our approach is based on a reduction to the realizable case, followed by a margin-based filtering of high-quality hypotheses. Furthermore, we show a nearly-matching lower bound, settling the sample complexity of agnostic boosting up to logarithmic factors.

翻译：提升是统计学习中的关键方法，能够将弱学习器转化为强学习器。虽然在可实现情形下已有深入研究，但在不可知设定下（即对标签分布不作任何假设），弱学习到强学习的统计特性仍鲜为人知。本研究中，我们提出了一种新的不可知提升算法，在非常一般的假设下，其样本复杂度相比先前工作有显著改善。我们的方法基于对可实现情形的归约，随后通过基于间隔的过滤机制筛选高质量假设。此外，我们给出了近乎匹配的下界，从而在忽略对数因子的意义上确定了不可知提升的样本复杂度。