Computationally Efficient Bayesian Learning of Gaussian Process State Space Models

7 June 2015

Andreas Svensson

Arno Solin

Simo Särkkä

Thomas B. Schon

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Papers citing "Computationally Efficient Bayesian Learning of Gaussian Process State Space Models"

26 / 26 papers shown

Title
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems Zhidi Lin Ying Li Feng Yin Juan Maroñas Alexandre Thiéry 54 0 0 24 Mar 2025
Adaptive Basis Function Selection for Computationally Efficient Predictions Anton Kullberg Frida Marie Viset Isaac Skog Gustaf Hendeby 28 0 0 14 Aug 2024
Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices Frida Viset Anton Kullberg Frederiek Wesel Arno Solin 32 0 0 05 Aug 2024
Training Bayesian Neural Networks with Sparse Subspace Variational Inference Junbo Li Zichen Miao Qiang Qiu Ruqi Zhang BDL UQCV 20 8 0 16 Feb 2024
Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference Zhidi Lin Yiyong Sun Feng Yin Alexandre Thiéry 32 4 0 10 Dec 2023
Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models Alonso Marco Elias Morley Claire Tomlin 36 2 0 13 Sep 2023
Free-Form Variational Inference for Gaussian Process State-Space Models Xuhui Fan Edwin V. Bonilla T. O’Kane Scott A. Sisson 16 9 0 20 Feb 2023
Sequential Estimation of Gaussian Process-based Deep State-Space Models Yuhao Liu Marzieh Ajirak P. Djuric 26 12 0 29 Jan 2023
Learning Nonparametric Volterra Kernels with Gaussian Processes M. Ross M. Smith Mauricio A. Alvarez GP 19 7 0 10 Jun 2021
Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes Alexander von Rohr Matthias Neumann-Brosig Sebastian Trimpe 22 8 0 17 May 2021
State-space aerodynamic model reveals high force control authority and predictability in flapping flight Y. Bayiz Bo Cheng 6 11 0 14 Mar 2021
Structure-preserving Gaussian Process Dynamics K. Ensinger Friedrich Solowjow Sebastian Ziesche Michael Tiemann Sebastian Trimpe 40 9 0 02 Feb 2021
The Use of Gaussian Processes in System Identification Simo Särkkä GP AI4TS 14 8 0 13 Jul 2019
Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks Qi She Anqi Wu BDL 20 34 0 01 Jul 2019
When Gaussian Process Meets Big Data: A Review of Scalable GPs Haitao Liu Yew-Soon Ong Xiaobo Shen Jianfei Cai GP 16 681 0 03 Jul 2018
A Data-Driven Approach to Dynamically Adjust Resource Allocation for Compute Clusters Francesco Pace Dimitrios Milios D. Carra D. Venzano Pietro Michiardi 6 4 0 01 Jul 2018
Learning dynamical systems with particle stochastic approximation EM Andreas Svensson Fredrik Lindsten 36 9 0 25 Jun 2018
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processes Cristian Guarnizo Lemus Mauricio A. Alvarez 11 15 0 18 May 2018
Learning unknown ODE models with Gaussian processes Markus Heinonen Çağatay Yıldız Henrik Mannerstrom Jukka Intosalmi Harri Lähdesmäki 24 93 0 12 Mar 2018
Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation Roman Föll B. Haasdonk Markus Hanselmann Holger Ulmer BDL 20 6 0 02 Nov 2017
Identification of Gaussian Process State Space Models Stefanos Eleftheriadis Tom Nicholson M. Deisenroth J. Hensman 26 111 0 30 May 2017
Spectral learning of dynamic systems from nonequilibrium data Hao Wu Frank Noé 21 4 0 04 Sep 2016
A flexible state space model for learning nonlinear dynamical systems Andreas Svensson Thomas B. Schon 21 104 0 17 Mar 2016
System Identification through Online Sparse Gaussian Process Regression with Input Noise Hildo Bijl Thomas B. Schon J. Wingerden M. Verhaegen 39 41 0 29 Jan 2016
Recurrent Gaussian Processes C. L. C. Mattos Zhenwen Dai Andreas C. Damianou Jeremy Forth G. Barreto Neil D. Lawrence BDL 32 75 0 20 Nov 2015
Nonlinear State Space Model Identification Using a Regularized Basis Function Expansion Andreas Svensson Thomas B. Schon Arno Solin Simo Särkkä 28 65 0 02 Oct 2015