4 papers
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…
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…
Optimal Parallelization of Boosting
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 and the total parallel work per r…
Boosting, Voting Classifiers and Randomized Sample Compression Schemes
Arthur da Cunha, Kasper Green Larsen, Martin Ritzert
In boosting, we aim to leverage multiple weak learners to produce a strong learner. At the center of this paradigm lies the concept of building the strong learner as a voting class…