4 papers
A One-Sided McDiarmid-Type Inequality for Heavy Tails via Reverse KL Tensorization
Hanuka Eshima, Amedeo Roberto Esposito, Andrea Paudice
We revisit McDiarmid's inequality, a central tool in statistical learning theory and algorithm analysis. We show that large deviations above the mean are exponentially unlikely for…
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…
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…
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…