1 citations · 4 across the 17 of their papers we have counts for
4 papers · 2 filters
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…
Efficient Optimal PAC Learning
Mikael Møller Høgsgaard
Recent advances in the binary classification setting by Hanneke [2016b] and Larsen [2023] have resulted in optimal PAC learners. These learners leverage, respectively, a clever det…
Improved Margin Generalization Bounds for Voting Classifiers
Mikael Møller Høgsgaard, Kasper Green Larsen
In this paper we establish a new margin-based generalization bound for voting classifiers, refining existing results and yielding tighter generalization guarantees for widely used…
On Agnostic PAC Learning in the Small Error Regime
Julian Asilis, Mikael Møller Høgsgaard, Grigoris Velegkas
Binary classification in the classic PAC model exhibits a curious phenomenon: Empirical Risk Minimization (ERM) learners are suboptimal in the realizable case yet optimal in the ag…