Revisiting Agnostic Boosting

• URL: http://arxiv.org/abs/2503.09384v1
• Date: Wed, 12 Mar 2025 13:29:33 GMT
• Title: Revisiting Agnostic Boosting
• Authors: Arthur da Cunha, Mikael Møller Høgsgaard, Andrea Paudice, Yuxin Sun,
• Abstract summary: We propose a new agnostic boosting algorithm with substantially improved sample complexity.<n>We conjecture that the error rate achieved by our proposed method is optimal up to logarithmic factors.
• Score: 6.913672545578391
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 remains 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 step to select high-quality hypotheses. We conjecture that the error rate achieved by our proposed method is optimal up to logarithmic factors.

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