Sample-Near-Optimal Agnostic Boosting with Improved Running Time

Arthur da Cunha, Mikael Møller Høgsgaard, Andrea Paudice

Keywords: boosting, agnostic learning, sample complexity

TL;DR: We provide the first agnostic boosting algorithm with near-optimal sample complexity and polynomial run time when the other parameters of the problem is considered fixed.

Abstract:

Boosting is a powerful method that turns weak learners, which perform only slightly better
than random guessing, into strong learners with high accuracy. While boosting is well
understood in the classic setting, it is less so in the agnostic case, where no assumptions
are made about the data. Indeed, only recently was the sample complexity of agnostic
boosting nearly settled (da Cunha et al., 2025), but the known algorithm achieving this
bound has exponential running time. In this work, we propose the first agnostic boosting
algorithm with near-optimal sample complexity, running in time polynomial in the sample
size when considering the other parameters of the problem fixed.

Submission Number: 100