Koopman-Equivariant Gaussian Processes

10 February 2025

Papers citing "Koopman-Equivariant Gaussian Processes"

50 / 61 papers shown

Title
MTIL: Encoding Full History with Mamba for Temporal Imitation Learning Yulin Zhou Yuankai Lin Fanzhe Peng Jiahui Chen Zhuang Zhou Kaiji Huang Hua Yang Zhouping Yin Mamba 36 0 0 18 May 2025
Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel Jialin Li Marta Zagorowska Giulia De Pasquale Alisa Rupenyan John Lygeros 48 3 0 26 Sep 2024
Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms Dimitri Meunier Zikai Shen Mattes Mollenhauer Arthur Gretton Zhu Li 82 5 0 23 May 2024
Safe Exploration Using Bayesian World Models and Log-Barrier Optimization Yarden As Bhavya Sukhija Andreas Krause OffRL 52 2 0 09 May 2024
Koopman operators with intrinsic observables in rigged reproducing kernel Hilbert spaces Isao Ishikawa Yuka Hashimoto Masahiro Ikeda Yoshinobu Kawahara 17 9 0 04 Mar 2024
Vanilla Bayesian Optimization Performs Great in High Dimensions Carl Hvarfner E. Hellsten Luigi Nardi 56 19 0 03 Feb 2024
Learning Symmetrization for Equivariance with Orbit Distance Minimization Tien Dat Nguyen Jinwoo Kim Hongseok Yang Seunghoon Hong 43 3 0 13 Nov 2023
Learning invariant representations of time-homogeneous stochastic dynamical systems Vladimir Kostic P. Novelli Riccardo Grazzi Karim Lounici Massimiliano Pontil 40 7 0 19 Jul 2023
Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance Jinwoo Kim Tien Dat Nguyen Ayhan Suleymanzade Hyeokjun An Seunghoon Hong 60 23 0 05 Jun 2023
Koopman Kernel Regression Petar Bevanda Maximilian Beier Armin Lederer Stefan Sosnowski Eyke Hüllermeier Sandra Hirche AI4TS 39 16 0 25 May 2023
The Signature Kernel Darrick Lee Harald Oberhauser 176 10 0 08 May 2023
Images of Gaussian and other stochastic processes under closed, densely-defined, unbounded linear operators T. Matsumoto T. Sullivan 50 3 0 05 May 2023
Free-Form Variational Inference for Gaussian Process State-Space Models Xuhui Fan Edwin V. Bonilla T. O’Kane Scott A. Sisson 38 9 0 20 Feb 2023
Sharp Spectral Rates for Koopman Operator Learning Vladimir Kostic Karim Lounici P. Novelli Massimiliano Pontil 89 21 0 03 Feb 2023
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation Henry B. Moss Sebastian W. Ober Victor Picheny 57 26 0 24 Jan 2023
The Past Does Matter: Correlation of Subsequent States in Trajectory Predictions of Gaussian Process Models Steffen Ridderbusch Sina Ober-Blobaum Paul Goulart 42 2 0 20 Nov 2022
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion Marin Bilos Kashif Rasul Anderson Schneider Yuriy Nevmyvaka Stephan Günnemann DiffM 54 33 0 04 Nov 2022
Gaussian Process Koopman Mode Decomposition Takahiro Kawashima H. Hino 34 6 0 09 Sep 2022
Optimal Rates for Regularized Conditional Mean Embedding Learning Zhu Li Dimitri Meunier Mattes Mollenhauer Arthur Gretton 45 49 0 02 Aug 2022
Ensemble forecasts in reproducing kernel Hilbert space family Benjamin Dufée Berenger Hug É. Mémin G. Tissot 75 1 0 29 Jul 2022
Safe Reinforcement Learning via Confidence-Based Filters Sebastian Curi Armin Lederer Sandra Hirche Andreas Krause OffRL 41 4 0 04 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 50 61 0 27 May 2022
Fast Instrument Learning with Faster Rates Ziyu Wang Yuhao Zhou Jun Zhu 71 3 0 22 May 2022
Efficiently Modeling Long Sequences with Structured State Spaces Albert Gu Karan Goel Christopher Ré 162 1,719 0 31 Oct 2021
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning Lukas Brunke Melissa Greeff Adam W. Hall Zhaocong Yuan Siqi Zhou Jacopo Panerati Angela P. Schoellig OffRL 48 610 0 13 Aug 2021
Provably Strict Generalisation Benefit for Invariance in Kernel Methods Bryn Elesedy 29 27 0 04 Jun 2021
GoSafe: Globally Optimal Safe Robot Learning Dominik Baumann A. Marco M. Turchetta Sebastian Trimpe 29 37 0 27 May 2021
Monash Time Series Forecasting Archive Rakshitha Godahewa Christoph Bergmeir Geoffrey I. Webb Rob J. Hyndman Pablo Montero-Manso AI4TS 39 150 0 14 May 2021
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data M. Lemercier C. Salvi Thomas Cass Edwin V. Bonilla Theodoros Damoulas Terry Lyons 37 25 0 10 May 2021
Modern Koopman Theory for Dynamical Systems Steven L. Brunton M. Budišić E. Kaiser J. Nathan Kutz AI4CE 90 411 0 24 Feb 2021
Provably Strict Generalisation Benefit for Equivariant Models Bryn Elesedy Sheheryar Zaidi AI4CE 26 85 0 20 Feb 2021
E(n) Equivariant Graph Neural Networks Victor Garcia Satorras Emiel Hoogeboom Max Welling 61 997 0 19 Feb 2021
On Information Gain and Regret Bounds in Gaussian Process Bandits Sattar Vakili Kia Khezeli Victor Picheny GP 46 130 0 15 Sep 2020
The Signature Kernel is the solution of a Goursat PDE C. Salvi Thomas Cass James Foster Terry Lyons Weixin Yang SyDa 149 57 0 26 Jun 2020
Prediction with Approximated Gaussian Process Dynamical Models Thomas Beckers Sandra Hirche AI4CE 29 19 0 25 Jun 2020
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning Sebastian Curi Felix Berkenkamp Andreas Krause 47 83 0 15 Jun 2020
Kernel Analog Forecasting: Multiscale Test Problems Dmitry Burov D. Giannakis Krithika Manohar Andrew M. Stuart 27 22 0 13 May 2020
Time Series Forecasting With Deep Learning: A Survey Bryan Lim S. Zohren AI4TS AI4CE 68 1,211 0 28 Apr 2020
D4RL: Datasets for Deep Data-Driven Reinforcement Learning Justin Fu Aviral Kumar Ofir Nachum George Tucker Sergey Levine GP OffRL 177 1,338 0 15 Apr 2020
On Simulation and Trajectory Prediction with Gaussian Process Dynamics Lukas Hewing Elena Arcari Lukas P. Frohlich Melanie Zeilinger 35 35 0 23 Dec 2019
Geometric Considerations of a Good Dictionary for Koopman Analysis of Dynamical Systems: Cardinality, 'Primary Eigenfunction,' and Efficient Representation Erik Bollt 20 23 0 18 Dec 2019
A Rigorous Theory of Conditional Mean Embeddings I. Klebanov Ingmar Schuster T. Sullivan 40 40 0 02 Dec 2019
Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 Omer Berat Sezer M. U. Gudelek A. Ozbayoglu AI4TS 60 1,001 0 29 Nov 2019
Stochastic data-driven model predictive control using Gaussian processes E. Bradford Lars Imsland Dongda Zhang Ehecatl Antonio del Rio Chanona 23 94 0 05 Aug 2019
Operator-theoretic framework for forecasting nonlinear time series with kernel analog techniques Romeo Alexander D. Giannakis AI4TS 8 66 0 30 May 2019
Rates of Convergence for Sparse Variational Gaussian Process Regression David R. Burt C. Rasmussen Mark van der Wilk 47 152 0 08 Mar 2019
Learning Invariances using the Marginal Likelihood Mark van der Wilk Matthias Bauer S. T. John J. Hensman 57 85 0 16 Aug 2018
Approximation beats concentration? An approximation view on inference with smooth radial kernels M. Belkin 77 69 0 10 Jan 2018
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces Stefan Klus Ingmar Schuster Krikamol Muandet 48 121 0 05 Dec 2017
Variational approach for learning Markov processes from time series data Hao Wu Frank Noé BDL AI4TS 22 261 0 14 Jul 2017