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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…
The Many Faces of Optimal Weak-to-Strong Learning
Mikael Møller Høgsgaard, Kasper Green Larsen, Markus Engelund Mathiasen
Boosting is an extremely successful idea, allowing one to combine multiple low accuracy classifiers into a much more accurate voting classifier. In this work, we present a new and…
Compression Implies Generalization
Allan Grønlund, Mikael Høgsgaard, Lior Kamma +1
Explaining the surprising generalization performance of deep neural networks is an active and important line of research in theoretical machine learning. Influential work by Arora…