Mikael Høgsgaard

According to our database, Mikael Høgsgaard authored at least 11 papers between 2023 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

On Agnostic PAC Learning in the Small Error Regime.

Julian Asilis, Mikael Møller Høgsgaard, Grigoris Velegkas

CoRR, February, 2025

Efficient Optimal PAC Learning.

Mikael Møller Høgsgaard

Proceedings of the International Conference on Algorithmic Learning Theory, 2025

Understanding Aggregations of Proper Learners in Multiclass Classification.

Julian Asilis, Mikael Møller Høgsgaard, Grigoris Velegkas

Proceedings of the International Conference on Algorithmic Learning Theory, 2025

2024

Majority-of-Three: The Simplest Optimal Learner?

Ishaq Aden-Ali, Mikael Møller Høgsgaard, Kasper Green Larsen, Nikita Zhivotovskiy

CoRR, 2024

The Many Faces of Optimal Weak-to-Strong Learning.

Mikael Møller Høgsgaard, Kasper Green Larsen, Markus Engelund Mathiasen

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Optimal Parallelization of Boosting.

Arthur da Cunha, Mikael Møller Høgsgaard, Kasper Green Larsen

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Sparse Dimensionality Reduction Revisited.

Mikael Møller Høgsgaard, Lior Kamma, Kasper Green Larsen, Jelani Nelson, Chris Schwiegelshohn

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Optimally Interpolating between Ex-Ante Fairness and Welfare.

Mikael Høgsgaard, Panagiotis Karras, Wenyue Ma, Nidhi Rathi, Chris Schwiegelshohn

CoRR, 2023

Barriers for Faster Dimensionality Reduction.

Ora Nova Fandina, Mikael Møller Høgsgaard, Kasper Green Larsen

Proceedings of the 40th International Symposium on Theoretical Aspects of Computer Science, 2023

AdaBoost is not an Optimal Weak to Strong Learner.

Mikael Møller Høgsgaard, Kasper Green Larsen, Martin Ritzert

Proceedings of the International Conference on Machine Learning, 2023

The Fast Johnson-Lindenstrauss Transform Is Even Faster.

Ora Nova Fandina, Mikael Møller Høgsgaard, Kasper Green Larsen

Proceedings of the International Conference on Machine Learning, 2023