Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces

5 December 2017

Papers citing "Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces"

22 / 22 papers shown

Title
Deep Koopman-layered Model with Universal Property Based on Toeplitz Matrices Yuka Hashimoto Tomoharu Iwata 28 0 0 03 Oct 2024
Dynamical systems and complex networks: A Koopman operator perspective Stefan Klus Natavsa Djurdjevac Conrad 16 2 0 14 May 2024
Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method Edoardo Caldarelli Antoine Chatalic Adrià Colomé C. Molinari C. Ocampo‐Martinez Carme Torras Lorenzo Rosasco 36 0 0 05 Mar 2024
Discovering Causal Relations and Equations from Data Gustau Camps-Valls Andreas Gerhardus Urmi Ninad Gherardo Varando Georg Martius E. Balaguer-Ballester Ricardo Vinuesa Emiliano Díaz L. Zanna Jakob Runge PINN AI4Cl AI4CE CML 35 73 0 21 May 2023
Propagating Kernel Ambiguity Sets in Nonlinear Data-driven Dynamics Models Jia-Jie Zhu 21 0 0 27 Apr 2023
Koopman-based generalization bound: New aspect for full-rank weights Yuka Hashimoto Sho Sonoda Isao Ishikawa Atsushi Nitanda Taiji Suzuki 11 2 0 12 Feb 2023
Online Estimation of the Koopman Operator Using Fourier Features Tahiya Salam Alice K. Li M. Hsieh 29 6 0 03 Dec 2022
Ensemble forecasts in reproducing kernel Hilbert space family Benjamin Dufée Berenger Hug É. Mémin G. Tissot 24 1 0 29 Jul 2022
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces Vladimir Kostic P. Novelli Andreas Maurer C. Ciliberto Lorenzo Rosasco Massimiliano Pontil 30 60 0 27 May 2022
Forward Operator Estimation in Generative Models with Kernel Transfer Operators Z. Huang Rudrasis Chakraborty Vikas Singh GAN 16 3 0 01 Dec 2021
Rigorous data-driven computation of spectral properties of Koopman operators for dynamical systems Matthew J. Colbrook Alex Townsend 26 67 0 29 Nov 2021
Deeptime: a Python library for machine learning dynamical models from time series data Moritz Hoffmann Martin K. Scherer Tim Hempel Andreas Mardt Brian M. de Silva ... Stefan Klus Hao Wu N. Kutz Steven L. Brunton Frank Noé AI4CE 33 101 0 28 Oct 2021
Convergence Rates for Learning Linear Operators from Noisy Data Maarten V. de Hoop Nikola B. Kovachki Nicholas H. Nelsen Andrew M. Stuart 19 54 0 27 Aug 2021
Sobolev Norm Learning Rates for Conditional Mean Embeddings Prem M. Talwai A. Shameli D. Simchi-Levi 24 10 0 16 May 2021
Estimating Koopman operators for nonlinear dynamical systems: a nonparametric approach Francesco Zanini A. Chiuso 12 5 0 25 Mar 2021
Modern Koopman Theory for Dynamical Systems Steven L. Brunton M. Budišić E. Kaiser J. Nathan Kutz AI4CE 46 392 0 24 Feb 2021
Learning Theory for Estimation of Animal Motion Submanifolds Nathan Powell A. Kurdila 11 4 0 30 Mar 2020
Data-driven approximation of the Koopman generator: Model reduction, system identification, and control Stefan Klus Feliks Nuske Sebastian Peitz Jan-Hendrik Niemann C. Clementi Christof Schütte 23 221 0 23 Sep 2019
Dimensionality Reduction of Complex Metastable Systems via Kernel Embeddings of Transition Manifolds A. Bittracher Stefan Klus B. Hamzi P. Koltai Christof Schütte 16 22 0 18 Apr 2019
MONK -- Outlier-Robust Mean Embedding Estimation by Median-of-Means M. Lerasle Z. Szabó Gaspar Massiot Guillaume Lecué 34 34 0 13 Feb 2018
Determinantal point processes for machine learning Alex Kulesza B. Taskar 176 1,123 0 25 Jul 2012
Conditional mean embeddings as regressors - supplementary Steffen Grunewalder Guy Lever Luca Baldassarre Sam Patterson Arthur Gretton Massimiliano Pontil 85 143 0 21 May 2012