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Variational Gaussian Process State-Space Models

18 June 2014

Papers citing "Variational Gaussian Process State-Space Models"

50 / 87 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 160 0 0 24 Mar 2025
Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis Yasamin Jalalian Juan Felipe Osorio Ramirez Alexander W. Hsu Bamdad Hosseini H. Owhadi 174 0 0 02 Mar 2025
Koopman-Equivariant Gaussian Processes Petar Bevanda Max Beier Armin Lederer A. Capone Stefan Sosnowski Sandra Hirche AI4TS 102 2 0 10 Feb 2025
Recursive Gaussian Process State Space Model Tengjie Zheng Lin Cheng Lin Cheng Shengping Gong Xu Huang 117 0 0 22 Nov 2024
Hybrid Data-Driven SSM for Interpretable and Label-Free mmWave Channel Prediction Yiyong Sun Jiajun He Zhidi Lin Wenqiang Pu Feng Yin Hing Cheung So 174 1 0 18 Nov 2024
Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems Amber Hu D. Zoltowski Aditya Nair David Anderson Lea Duncker Scott W. Linderman 120 3 0 19 Jul 2024
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes Markus Lange-Hegermann Christoph Zimmer AI4TS 124 0 0 17 May 2024
Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference Zhidi Lin Yiyong Sun Feng Yin Alexandre Thiéry 65 4 0 10 Dec 2023
A projected nonlinear state-space model for forecasting time series signals Christian Donner Anuj Mishra Hideaki Shimazaki AI4TS 61 0 0 22 Nov 2023
Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models Alonso Marco Elias Morley Claire Tomlin 88 3 0 13 Sep 2023
Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models Zhidi Lin Juan Maroñas Ying Li Feng Yin Sergios Theodoridis 82 3 0 03 Sep 2023
Multi-Response Heteroscedastic Gaussian Process Models and Their Inference Taehee Lee Jun S. Liu 40 1 0 29 Aug 2023
The Bayesian Context Trees State Space Model for time series modelling and forecasting I. Papageorgiou Ioannis Kontoyiannis AI4TS 60 2 0 02 Aug 2023
Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains Matthew Dowling Yuan Zhao Il Memming Park 59 6 0 01 Jun 2023
Variational Nonlinear Kalman Filtering with Unknown Process Noise Covariance Hua Lan Jinjie Hu Zengfu Wang Q. Cheng 46 11 0 06 May 2023
Free-Form Variational Inference for Gaussian Process State-Space Models Xuhui Fan Edwin V. Bonilla T. O’Kane Scott A. Sisson 66 9 0 20 Feb 2023
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization JIAN-PENG Cao Myeongjong Kang Felix Jimenez H. Sang Florian Schäfer Matthias Katzfuss 66 7 0 30 Jan 2023
Deep networks for system identification: a Survey G. Pillonetto Aleksandr Aravkin Daniel Gedon L. Ljung Antônio H. Ribeiro Thomas B. Schon OOD 98 44 0 30 Jan 2023
Sequential Estimation of Gaussian Process-based Deep State-Space Models Yuhao Liu Marzieh Ajirak Petar M. Djurić 96 14 0 29 Jan 2023
Towards Flexibility and Interpretability of Gaussian Process State-Space Model Zhidi Lin Feng Yin Juan Maroñas 106 7 0 21 Jan 2023
Output-Dependent Gaussian Process State-Space Model Zhidi Lin Lei Cheng Feng Yin Le Xu Shuguang Cui UQCV 80 5 0 15 Dec 2022
Recurrent Neural Networks and Universal Approximation of Bayesian Filters A. Bishop Edwin V. Bonilla BDL 73 3 0 01 Nov 2022
Continual Learning of Multi-modal Dynamics with External Memory Abdullah Akgul Gözde B. Ünal M. Kandemir CLL 46 0 0 02 Mar 2022
Conditional Approximate Normalizing Flows for Joint Multi-Step Probabilistic Forecasting with Application to Electricity Demand Arec Jamgochian Di Wu Kunal Menda Soyeon Jung Mykel J. Kochenderfer AI4TS 34 2 0 08 Jan 2022
Likelihood-Free Inference in State-Space Models with Unknown Dynamics Alexander Aushev Thong Tran Henri Pesonen Andrew Howes Samuel Kaski 54 1 0 02 Nov 2021
Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equation Arno Solin Ella Tamir Prakhar Verma 54 19 0 29 Oct 2021
Active Learning in Gaussian Process State Space Model H. Yu Dingling Yao Christoph Zimmer Marc Toussaint D. Nguyen-Tuong GP 43 4 0 30 Jul 2021
A Framework for Machine Learning of Model Error in Dynamical Systems Matthew E. Levine Andrew M. Stuart 98 69 0 14 Jul 2021
Inferring the Structure of Ordinary Differential Equations Juliane Weilbach S. Gerwinn Christian D. Weilbach M. Kandemir 59 3 0 05 Jul 2021
Variational multiple shooting for Bayesian ODEs with Gaussian processes Pashupati Hegde Çağatay Yıldız Harri Lähdesmäki Samuel Kaski Markus Heinonen 76 16 0 21 Jun 2021
Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems Haitao Liu Changjun Liu Xiaomo Jiang Xudong Chen Shuhua Yang Xiaofang Wang BDL AI4TS 93 2 0 03 Jun 2021
Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes Alexander von Rohr Matthias Neumann-Brosig Sebastian Trimpe 51 8 0 17 May 2021
Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian Approach A. Lee Panagiotis Lymperopoulos Joshua T. Cohen J. Wong Michael C. Hughes 11 1 0 14 Apr 2021
State-space aerodynamic model reveals high force control authority and predictability in flapping flight Y. Bayiz Bo Cheng 38 11 0 14 Mar 2021
Keep it Simple: Data-efficient Learning for Controlling Complex Systems with Simple Models Thomas Power Dmitry Berenson 55 16 0 04 Feb 2021
Structure-preserving Gaussian Process Dynamics K. Ensinger Friedrich Solowjow Sebastian Ziesche Michael Tiemann Sebastian Trimpe 71 9 0 02 Feb 2021
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting Seungjae Jung KyungHyun Kim Hanock Kwak Young-Jin Park AI4TS 23 6 0 21 Nov 2020
Stochastic embeddings of dynamical phenomena through variational autoencoders C. A. García P. Félix J. Presedo A. Otero BDL 53 2 0 13 Oct 2020
Variational Filtering with Copula Models for SLAM John D. Martin Kevin Doherty Caralyn Cyr Brendan Englot J. Leonard 51 3 0 02 Aug 2020
Learning Unstable Dynamical Systems with Time-Weighted Logarithmic Loss Kamil Nar Yuan Xue Andrew M. Dai 38 2 0 10 Jul 2020
Sparse Gaussian Processes with Spherical Harmonic Features Vincent Dutordoir N. Durrande J. Hensman 71 56 0 30 Jun 2020
Prediction with Approximated Gaussian Process Dynamical Models Thomas Beckers Sandra Hirche AI4CE 57 19 0 25 Jun 2020
Neural Physicist: Learning Physical Dynamics from Image Sequences Baocheng Zhu Shijun Wang James Y. Zhang AI4CE 23 1 0 09 Jun 2020
Bayesian Hidden Physics Models: Uncertainty Quantification for Discovery of Nonlinear Partial Differential Operators from Data Steven Atkinson 42 8 0 07 Jun 2020
Deep Latent-Variable Kernel Learning Haitao Liu Yew-Soon Ong Xiaomo Jiang Xiaofang Wang BDL 59 8 0 18 May 2020
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing Feng Yin Zhidi Lin Yue Xu Qinglei Kong Deshi Li Sergios Theodoridis Shuguang Cui Cui FedML 135 4 0 08 Mar 2020
Safe Interactive Model-Based Learning Marco Gallieri Seyed Sina Mirrazavi Salehian N. E. Toklu A. Quaglino Jonathan Masci Jan Koutník Faustino J. Gomez 70 12 0 15 Nov 2019
Structured Variational Inference in Unstable Gaussian Process State Space Models Silvan Melchior Sebastian Curi Felix Berkenkamp Andreas Krause 76 4 0 16 Jul 2019
The Use of Gaussian Processes in System Identification Simo Särkkä GP AI4TS 40 10 0 13 Jul 2019
Learning GPLVM with arbitrary kernels using the unscented transformation Daniel Augusto R. M. A. de Souza Diego Mesquita C. L. C. Mattos Joao P. P. Gomes 54 0 0 03 Jul 2019