提出新方法，显著降低非可实现情况下的样本需求。
Revisiting Agnostic Boosting
- 将非可实现问题转化为可实现问题，再通过置信度筛选高质量模型
- 样本复杂度优于以往方法，在一般假设下表现更优
- 证明了接近最优的下界，解决了该领域关键理论问题
提升是统计学习中的关键方法，可将弱学习器转化为强学习器。尽管在可实现情况下已有深入研究，但在无标签分布假设的非可实现设置下，弱到强学习的统计性质仍不清晰。本文提出一种新的非可实现提升算法，在极一般假设下样本复杂度显著优于先前工作。该方法基于将问题归约为可实现情形，并结合基于边界的质量过滤机制。此外，我们给出了几乎匹配的下界，将非可实现提升的样本复杂度确定至对数因子范围内。
原文摘要 · Abstract (English)
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.
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