Mattes Mollenhauer

According to our database, Mattes Mollenhauer authored at least 12 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Float8@2bits: Entropy Coding Enables Data-Free Model Compression.

Patrick Putzky, Martin Genzel, Mattes Mollenhauer, Sebastian Schulze, Thomas Wollmann, Stefan Dietzel

CoRR, January, 2026

2025

Choose Your Model Size: Any Compression by a Single Gradient Descent.

Martin Genzel, Patrick Putzky, Pengfei Zhao, Sebastian Schulze, Mattes Mollenhauer, Robert Seidel, Stefan Dietzel, Thomas Wollmann

CoRR, February, 2025

Choose Your Model Size: Any Compression of Large Language Models Without Re-Computation.

Martin Genzel, Patrick Putzky, Pengfei Zhao, Sebastian Schulze, Mattes Mollenhauer, Robert Seidel, Stefan Dietzel, Thomas Wollmann

Trans. Mach. Learn. Res., 2025

Regularized least squares learning with heavy-tailed noise is minimax optimal.

Mattes Mollenhauer, Nicole Mücke, Dimitri Meunier, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

2024

Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm.

Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton

J. Mach. Learn. Res., 2024

Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms.

Dimitri Meunier, Zikai Shen, Mattes Mollenhauer, Arthur Gretton, Zhu Li

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Optimal Reaction Coordinates: Variational Characterization and Sparse Computation.

Andreas Bittracher, Mattes Mollenhauer, Péter Koltai, Christof Schütte

Multiscale Model. Simul., June, 2023

2022

Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence.

Mattes Mollenhauer, Stefan Klus, Christof Schütte, Péter Koltai

J. Mach. Learn. Res., 2022

Optimal Rates for Regularized Conditional Mean Embedding Learning.

Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2020

Nonparametric approximation of conditional expectation operators.

Mattes Mollenhauer, Péter Koltai

CoRR, 2020

Kernel Conditional Density Operators.

Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2018

On Hyperparameter Search in Cluster Ensembles.

Luzie Helfmann, Johannes von Lindheim, Mattes Mollenhauer, Ralf Banisch

CoRR, 2018