no code implementations • 12 Mar 2025 • Arthur da Cunha, Mikael Møller Høgsgaard, Andrea Paudice, Yuxin Sun
Boosting is a key method in statistical learning, allowing for converting weak learners into strong ones.
no code implementations • 29 Aug 2024 • Arthur da Cunha, Mikael Møller Høgsgaard, Kasper Green Larsen
Recent works on the parallel complexity of Boosting have established strong lower bounds on the tradeoff between the number of training rounds $p$ and the total parallel work per round $t$.
no code implementations • 5 Feb 2024 • Arthur da Cunha, Kasper Green Larsen, Martin Ritzert
At the center of this paradigm lies the concept of building the strong learner as a voting classifier, which outputs a weighted majority vote of the weak learners.
no code implementations • ICLR 2022 • Arthur da Cunha, Emanuele Natale, Laurent Viennot
The lottery ticket hypothesis states that a randomly-initialized neural network contains a small subnetwork such that, when trained in isolation, can compete with the performance of the original network.
