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Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces

5 December 2017

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

50 / 58 papers shown

Title
Sequence Modeling with Spectral Mean Flows Jinwoo Kim Max Beier Nicolas Hoischen Nayun Kim Seunghoon Hong BDL 138 0 0 17 Oct 2025
Bayesian Transfer Operators in Reproducing Kernel Hilbert Spaces Septimus Boshoff Sebastian Peitz Stefan Klus 93 0 0 26 Sep 2025
How deep is your network? Deep vs. shallow learning of transfer operators Mohammad Tabish Benedict Leimkuhler Stefan Klus 77 0 0 24 Sep 2025
Data-driven approximation of transfer operators for mean-field stochastic differential equations Eirini Ioannou Stefan Klus Gonçalo dos Reis 49 0 0 11 Sep 2025
Koopman-Equivariant Gaussian ProcessesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025 Nicolas Hoischen Max Beier Armin Lederer A. Capone Roland Toth Sandra Hirche AI4TS 250 5 0 10 Feb 2025
Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups Vladimir Kostic Karim Lounici Helene Halconruy Timothée Devergne P. Novelli Massimiliano Pontil 238 2 0 18 Oct 2024
Deep Koopman-layered Model with Universal Property Based on Toeplitz Matrices Yuka Hashimoto Tomoharu Iwata 337 0 0 03 Oct 2024
Inferring Kernel $ε$ -Machines: Discovering Structure in Complex SystemsChaos (Chaos), 2024 Alexandra M. Jurgens Nicolas Brodu CML 231 0 0 01 Oct 2024
Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces Nicolas Hoischen Nicolas Hoischen Roland Toth Sandra Hirche Boris Houska 281 2 0 23 Jul 2024
Learning the Infinitesimal Generator of Stochastic Diffusion Processes Vladimir Kostic Karim Lounici Helene Halconruy Timothée Devergne Massimiliano Pontil DiffM 161 7 0 21 May 2024
Dynamical systems and complex networks: A Koopman operator perspective Stefan Klus Natavsa Djurdjevac Conrad 80 10 0 14 May 2024
Nonparametric Control Koopman Operators Nicolas Hoischen Bas Driessen Lucian-Cristian Iacob Stefan Sosnowski Roland Toth Sandra Hirche 349 6 0 12 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 376 3 0 05 Mar 2024
Consistent Long-Term Forecasting of Ergodic Dynamical Systems Prune Inzerilli Vladimir Kostic Karim Lounici P. Novelli Massimiliano Pontil 184 8 0 20 Dec 2023
Operator-Based Detecting, Learning, and Stabilizing Unstable Periodic Orbits of Chaotic AttractorsAmerican Control Conference (ACC), 2023 A. Tavasoli Heman Shakeri 120 0 0 07 Sep 2023
Learning invariant representations of time-homogeneous stochastic dynamical systemsInternational Conference on Learning Representations (ICLR), 2023 Vladimir Kostic P. Novelli Riccardo Grazzi Karim Lounici Massimiliano Pontil 212 12 0 19 Jul 2023
Estimating Koopman operators with sketching to provably learn large scale dynamical systemsNeural Information Processing Systems (NeurIPS), 2023 Giacomo Meanti Antoine Chatalic Vladimir Kostic P. Novelli Massimiliano Pontil Lorenzo Rosasco 250 14 0 07 Jun 2023
Koopman Kernel RegressionNeural Information Processing Systems (NeurIPS), 2023 Nicolas Hoischen Maximilian Beier Armin Lederer Roland Toth Eyke Hüllermeier Sandra Hirche AI4TS 181 25 0 25 May 2023
Discovering Causal Relations and Equations from DataPhysics reports (Phys. Rep.), 2023 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 202 105 0 21 May 2023
Propagating Kernel Ambiguity Sets in Nonlinear Data-driven Dynamics Models Jia-Jie Zhu 182 0 0 27 Apr 2023
Koopman-based generalization bound: New aspect for full-rank weightsInternational Conference on Learning Representations (ICLR), 2023 Yuka Hashimoto Sho Sonoda Isao Ishikawa Atsushi Nitanda Taiji Suzuki 242 4 0 12 Feb 2023
Online Estimation of the Koopman Operator Using Fourier FeaturesConference on Learning for Dynamics & Control (L4DC), 2022 Tahiya Salam Alice K. Li M. Hsieh 197 7 0 03 Dec 2022
Ensemble forecasts in reproducing kernel Hilbert space family Benjamin Dufée Berenger Hug É. Mémin G. Tissot 394 2 0 29 Jul 2022
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert SpacesNeural Information Processing Systems (NeurIPS), 2022 Vladimir Kostic P. Novelli Andreas Maurer C. Ciliberto Lorenzo Rosasco Massimiliano Pontil 306 78 0 27 May 2022
Koopman-based spectral clustering of directed and time-evolving graphsJournal of nonlinear science (J. Nonlinear Sci.), 2022 Stefan Klus Nataša Djurdjevac Conrad 111 13 0 06 Apr 2022
Koopman Methods for Estimation of Animal Motions over Unknown Submanifolds Nathan Powell Bowei Liu Jia Guo Sai Tej Parachuri A. Kurdila 231 0 0 10 Mar 2022
KPF-AE-LSTM: A Deep Probabilistic Model for Net-Load Forecasting in High Solar Scenarios Deepthi Sen Indrasis Chakraborty Soumya Kundu A. Reiman I. Beil Andy Eiden 116 2 0 05 Mar 2022
Forward Operator Estimation in Generative Models with Kernel Transfer Operators Z. Huang Rudrasis Chakraborty Vikas Singh GAN 99 3 0 01 Dec 2021
Rigorous data-driven computation of spectral properties of Koopman operators for dynamical systems Matthew J. Colbrook Alex Townsend 315 95 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 206 130 0 28 Oct 2021
Learning and Leveraging Features in Flow-Like Environments to Improve Situational Awareness Tahiya Salam Victoria Edwards M. A. Hsieh 257 7 0 13 Sep 2021
Convergence Rates for Learning Linear Operators from Noisy Data Maarten V. de Hoop Nikola B. Kovachki Nicholas H. Nelsen Andrew M. Stuart 370 64 0 27 Aug 2021
Sobolev Norm Learning Rates for Conditional Mean EmbeddingsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021 Prem M. Talwai A. Shameli D. Simchi-Levi 259 12 0 16 May 2021
Learning Good State and Action Representations via Tensor DecompositionJournal of machine learning research (JMLR), 2021 Chengzhuo Ni Yaqi Duan M. Dahleh Anru R. Zhang Mengdi Wang 237 7 0 03 May 2021
Estimating Koopman operators for nonlinear dynamical systems: a nonparametric approachIFAC-PapersOnLine (IFAC-PapersOnLine), 2021 Francesco Zanini A. Chiuso 86 6 0 25 Mar 2021
Modern Koopman Theory for Dynamical SystemsSIAM Review (SIAM Rev.), 2021 Steven L. Brunton M. Budišić E. Kaiser J. Nathan Kutz AI4CE 231 535 0 24 Feb 2021
Reproducing kernel Hilbert C*-module and kernel mean embeddingsJournal of machine learning research (JMLR), 2021 Yuka Hashimoto Isao Ishikawa Masahiro Ikeda Fuyuta Komura Takeshi Katsura Yoshinobu Kawahara 117 13 0 27 Jan 2021
Nonparametric approximation of conditional expectation operators Mattes Mollenhauer P. Koltai 264 21 0 23 Dec 2020
Feature space approximation for kernel-based supervised learningKnowledge-Based Systems (KBS), 2020 Patrick Gelß Stefan Klus Ingmar Schuster Christof Schütte 98 5 0 25 Nov 2020
Discovering Causal Structure with Reproducing-Kernel Hilbert Space $ε$ -MachinesChaos (CHAOS), 2020 N. Brodu James P. Crutchfield CML 321 16 0 23 Nov 2020
The linear conditional expectation in Hilbert space I. Klebanov Björn Sprungk T. Sullivan 166 14 0 27 Aug 2020
GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome AnalysisApplied Network Science (Appl Netw Sci), 2020 K. Melnyk Stefan Klus G. Montavon Tim Conrad 120 11 0 12 Aug 2020
Kernel Mean Embeddings of Von Neumann-Algebra-Valued Measures Yuka Hashimoto Isao Ishikawa Masahiro Ikeda Fuyuta Komura Yoshinobu Kawahara 87 2 0 29 Jul 2020
Kernel-based approximation of the Koopman generator and Schrödinger operatorEntropy (Entropy), 2020 Stefan Klus Feliks Nuske B. Hamzi 140 71 0 27 May 2020
A Kernel Two-sample Test for Dynamical Systems Friedrich Solowjow Dominik Baumann Christian Fiedler Andreas J. Jocham Thomas Seel Sebastian Trimpe 193 11 0 23 Apr 2020
Kernel Autocovariance Operators of Stationary Processes: Estimation and ConvergenceJournal of machine learning research (JMLR), 2020 Mattes Mollenhauer Stefan Klus Christof Schütte P. Koltai 183 11 0 02 Apr 2020
Learning Theory for Estimation of Animal Motion SubmanifoldsIEEE Conference on Decision and Control (CDC), 2020 Nathan Powell A. Kurdila 66 4 0 30 Mar 2020
Analysis via Orthonormal Systems in Reproducing Kernel Hilbert $C^*$ -Modules and Applications Yuka Hashimoto Isao Ishikawa Masahiro Ikeda Fuyuta Komura Takeshi Katsura Yoshinobu Kawahara 99 2 0 02 Mar 2020
Geometric Considerations of a Good Dictionary for Koopman Analysis of Dynamical Systems: Cardinality, 'Primary Eigenfunction,' and Efficient RepresentationCommunications in nonlinear science & numerical simulation (CNSNS), 2019 Erik Bollt 73 25 0 18 Dec 2019
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 226 262 0 23 Sep 2019