On Hyperparameter Search in Cluster Ensembles. - Luzie Helfmann; Johannes von Lindheim; Mattes Mollenhauer; Ralf Banisch (CoRR, 2018)

Kernel Conditional Density Operators. - Ingmar Schuster; Mattes Mollenhauer; Stefan Klus; Krikamol Muandet (AISTATS, 2020)

Nonparametric approximation of conditional expectation operators. - Mattes Mollenhauer; Péter Koltai (CoRR, 2020)

Optimal Rates for Regularized Conditional Mean Embedding Learning. - Zhu Li; Dimitri Meunier; Mattes Mollenhauer; Arthur Gretton (NeurIPS, 2022)

Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence. - Mattes Mollenhauer; Stefan Klus; Christof Schütte; Péter Koltai (J. Mach. Learn. Res., 2022)

Optimal Reaction Coordinates: Variational Characterization and Sparse Computation. - Andreas Bittracher; Mattes Mollenhauer; Péter Koltai; Christof Schütte (Multiscale Model. Simul., 2023)

Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms. - Dimitri Meunier; Zikai Shen; Mattes Mollenhauer; Arthur Gretton; Zhu Li (NeurIPS, 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)

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, 2025)

Regularized least squares learning with heavy-tailed noise is minimax optimal. - Mattes Mollenhauer; Nicole Mücke; Dimitri Meunier; Arthur Gretton (CoRR, 2025)