3 papers
cs.LG2026
Sample-Near-Optimal Agnostic Boosting with Improved Running Time
Arthur da Cunha, Mikael Møller Høgsgaard, Andrea Paudice
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 und…
cs.LG2025
Revisiting Agnostic Boosting
Arthur da Cunha, Mikael Møller Høgsgaard, Andrea Paudice +1
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 w…
stat.ML2025
Uniform Mean Estimation for Heavy-Tailed Distributions via Median-of-Means
Mikael Møller Høgsgaard, Andrea Paudice
The Median of Means (MoM) is a mean estimator that has gained popularity in the context of heavy-tailed data. In this work, we analyze its performance in the task of simultaneously…