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arXiv · 2503.09384

Revisiting Agnostic Boosting

Arthur da Cunha·Mikael Møller Høgsgaard·Andrea Paudice·Yuxin Sun
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Abstract

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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Arthur da Cunha, Mikael Møller Høgsgaard, Andrea Paudice, Yuxin Sun. 2025-03-12. Revisiting Agnostic Boosting. https://arxiv.org/abs/2503.09384

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