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Articles by Mattes Mollenhauer

Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm
6 August 2024 by Zhu Li and others
Machine Learning
Learning linear operators: Infinite-dimensional regression as a well-behaved non-compact inverse problem
10 July 2024 by Mattes Mollenhauer and others
Statistics Theory, Functional Analysis
Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms
23 May 2024 by Dimitri Meunier and others at LogoUniversity College London
Machine Learning
Optimal Rates for Regularized Conditional Mean Embedding Learning
12 December 2023 by Zhu Li and others
Machine Learning
On the concentration of subgaussian vectors and positive quadratic forms in Hilbert spaces
3 October 2023 by Mattes Mollenhauer and Claudia Schillings
Probability
Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence
29 November 2022 by Mattes Mollenhauer and others
Probability, Machine Learning
Optimal Reaction Coordinates: Variational Characterization and Sparse Computation
22 September 2021 by Andreas Bittracher and others
Dynamical Systems
Nonparametric approximation of conditional expectation operators
7 January 2021 by Mattes Mollenhauer and Péter Koltai
Statistics Theory, Numerical Analysis
Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces
16 March 2020 by Mattes Mollenhauer and others
Functional Analysis, Machine Learning
Kernel Conditional Density Operators
29 October 2019 by Ingmar Schuster and others
Machine Learning, Statistics Theory
Kernel methods for detecting coherent structures in dynamical data
8 October 2019 by Stefan Klus and others
Dynamical Systems, Machine Learning
On Hyperparameter Search in Cluster Ensembles
29 March 2018 by Luzie Helfmann and others
Machine Learning