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System Identification through Online Sparse Gaussian Process Regression
  with Input Noise
v1v2v3 (latest)

System Identification through Online Sparse Gaussian Process Regression with Input Noise

29 January 2016
Hildo Bijl
Thomas B. Schon
J. Wingerden
M. Verhaegen
ArXiv (abs)PDFHTML

Papers citing "System Identification through Online Sparse Gaussian Process Regression with Input Noise"

22 / 22 papers shown
LILAD: Learning In-context Lyapunov-stable Adaptive Dynamics Models
LILAD: Learning In-context Lyapunov-stable Adaptive Dynamics Models
Amit Jena
Na Li
Le Xie
102
0
0
26 Nov 2025
Recursive Gaussian Process State Space Model
Recursive Gaussian Process State Space Model
Tengjie Zheng
Lin Cheng
Lin Cheng
Shengping Gong
Xu Huang
481
0
0
22 Nov 2024
Crafting with a Robot Assistant: Use Social Cues to Inform Adaptive
  Handovers in Human-Robot Collaboration
Crafting with a Robot Assistant: Use Social Cues to Inform Adaptive Handovers in Human-Robot CollaborationIEEE/ACM International Conference on Human-Robot Interaction (HRI), 2023
Leimin Tian
Kerry He
Shiyu Xu
Akansel Cosgun
Dana Kulić
288
12
0
07 Jan 2023
System identification using Bayesian neural networks with nonparametric
  noise models
System identification using Bayesian neural networks with nonparametric noise models
Christos Merkatas
Simo Särkkä
176
3
0
25 Apr 2021
Real-Time Regression with Dividing Local Gaussian Processes
Real-Time Regression with Dividing Local Gaussian Processes
Armin Lederer
Alejandro Jose Ordóñez Conejo
K. Maier
Wenxin Xiao
Jonas Umlauft
Sandra Hirche
249
12
0
16 Jun 2020
Probabilistic Regressor Chains with Monte Carlo Methods
Probabilistic Regressor Chains with Monte Carlo Methods
Jesse Read
Luca Martino
BDLUQCVAI4CELRM
251
15
0
18 Jul 2019
Physics Enhanced Data-Driven Models with Variational Gaussian Processes
Physics Enhanced Data-Driven Models with Variational Gaussian Processes
Daniel L. Marino
Milos Manic
142
3
0
05 Jun 2019
Streaming Variational Monte Carlo
Streaming Variational Monte CarloIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019
Yuan Zhao
Josue Nassar
I. Jordan
M. Bugallo
Il Memming Park
BDL
596
23
0
04 Jun 2019
Evaluating the squared-exponential covariance function in Gaussian
  processes with integral observations
Evaluating the squared-exponential covariance function in Gaussian processes with integral observations
J. Hendriks
Carl Jidling
A. Wills
Thomas B. Schon
209
9
0
18 Dec 2018
When Gaussian Process Meets Big Data: A Review of Scalable GPs
When Gaussian Process Meets Big Data: A Review of Scalable GPsIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2018
Haitao Liu
Yew-Soon Ong
Xiaobo Shen
Jianfei Cai
GP
558
841
0
03 Jul 2018
A flexible state space model for learning nonlinear dynamical systems
A flexible state space model for learning nonlinear dynamical systems
Andreas Svensson
Thomas B. Schon
279
111
0
17 Mar 2016
Computationally Efficient Bayesian Learning of Gaussian Process State
  Space Models
Computationally Efficient Bayesian Learning of Gaussian Process State Space ModelsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2015
Andreas Svensson
Arno Solin
Simo Särkkä
Thomas B. Schon
222
65
0
07 Jun 2015
Domain-Adversarial Training of Neural Networks
Domain-Adversarial Training of Neural NetworksJournal of machine learning research (JMLR), 2015
Yaroslav Ganin
E. Ustinova
Hana Ajakan
Pascal Germain
Hugo Larochelle
François Laviolette
M. Marchand
Victor Lempitsky
GANOOD
1.3K
11,039
0
28 May 2015
Distributed Gaussian Processes
Distributed Gaussian Processes
M. Deisenroth
Jun Wei Ng
GP
451
375
0
10 Feb 2015
Variational Gaussian Process State-Space Models
Variational Gaussian Process State-Space ModelsNeural Information Processing Systems (NeurIPS), 2014
R. Frigola
Yutian Chen
C. Rasmussen
BDL
354
188
0
18 Jun 2014
Distributed Variational Inference in Sparse Gaussian Process Regression
  and Latent Variable Models
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable ModelsNeural Information Processing Systems (NeurIPS), 2014
Y. Gal
Mark van der Wilk
C. Rasmussen
287
153
0
06 Feb 2014
Gaussian Processes for Big Data
Gaussian Processes for Big DataConference on Uncertainty in Artificial Intelligence (UAI), 2013
J. Hensman
Nicolò Fusi
Neil D. Lawrence
GP
623
1,336
0
26 Sep 2013
Bayesian Inference and Learning in Gaussian Process State-Space Models
  with Particle MCMC
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMCNeural Information Processing Systems (NeurIPS), 2013
R. Frigola
Fredrik Lindsten
Thomas B. Schon
C. Rasmussen
334
164
0
12 Jun 2013
Gaussian Process Regression with Heteroscedastic or Non-Gaussian
  Residuals
Gaussian Process Regression with Heteroscedastic or Non-Gaussian Residuals
Chunyi Wang
Radford M. Neal
296
52
0
26 Dec 2012
Variable noise and dimensionality reduction for sparse Gaussian
  processes
Variable noise and dimensionality reduction for sparse Gaussian processesConference on Uncertainty in Artificial Intelligence (UAI), 2006
Edward Snelson
Zoubin Ghahramani
224
81
0
27 Jun 2012
A Framework for Evaluating Approximation Methods for Gaussian Process
  Regression
A Framework for Evaluating Approximation Methods for Gaussian Process RegressionJournal of machine learning research (JMLR), 2012
Krzysztof Chalupka
Christopher K. I. Williams
Iain Murray
GP
360
184
0
29 May 2012
Kernels for Vector-Valued Functions: a Review
Kernels for Vector-Valued Functions: a Review
Mauricio A. Alvarez
Lorenzo Rosasco
Neil D. Lawrence
GP
638
1,029
0
30 Jun 2011
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