The Fast Johnson-Lindenstrauss Transform Is Even Faster. - Ora Nova Fandina; Mikael Møller Høgsgaard; Kasper Green Larsen (ICML, 2023)

AdaBoost is not an Optimal Weak to Strong Learner. - Mikael Møller Høgsgaard; Kasper Green Larsen; Martin Ritzert (ICML, 2023)

Barriers for Faster Dimensionality Reduction. - Ora Nova Fandina; Mikael Møller Høgsgaard; Kasper Green Larsen (STACS, 2023)

Optimally Interpolating between Ex-Ante Fairness and Welfare. - Mikael Høgsgaard; Panagiotis Karras; Wenyue Ma 001; Nidhi Rathi; Chris Schwiegelshohn (CoRR, 2023)

Sparse Dimensionality Reduction Revisited. - Mikael Møller Høgsgaard; Lior Kamma; Kasper Green Larsen; Jelani Nelson; Chris Schwiegelshohn (ICML, 2024)

Optimal Parallelization of Boosting. - Arthur da Cunha 001; Mikael Møller Høgsgaard; Kasper Green Larsen (NeurIPS, 2024)

The Many Faces of Optimal Weak-to-Strong Learning. - Mikael Møller Høgsgaard; Kasper Green Larsen; Markus Engelund Mathiasen (NeurIPS, 2024)

Majority-of-Three: The Simplest Optimal Learner? - Ishaq Aden-Ali; Mikael Møller Høgsgaard; Kasper Green Larsen; Nikita Zhivotovskiy (CoRR, 2024)

Understanding Aggregations of Proper Learners in Multiclass Classification. - Julian Asilis; Mikael Møller Høgsgaard; Grigoris Velegkas (ALT, 2025)

Efficient Optimal PAC Learning. - Mikael Møller Høgsgaard (ALT, 2025)

Improved Margin Generalization Bounds for Voting Classifiers. - Mikael Møller Høgsgaard; Kasper Green Larsen (COLT, 2025)

On Agnostic PAC Learning in the Small Error Regime. - Julian Asilis; Mikael Møller Høgsgaard; Grigoris Velegkas (CoRR, 2025)

Revisiting Agnostic Boosting. - Arthur da Cunha 001; Mikael Møller Høgsgaard; Andrea Paudice; Yuxin Sun (CoRR, 2025)

Uniform Mean Estimation for Heavy-Tailed Distributions via Median-of-Means. - Mikael Møller Høgsgaard; Andrea Paudice (CoRR, 2025)