# Difference between revisions of "GP SSM"

From Robotics

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* E.D. Klenske, P. Hennig, [[Media:DualControlApproxBayesianRL.pdf | Dual Control for Approximate Bayesian Reinforcement Learning]] | * E.D. Klenske, P. Hennig, [[Media:DualControlApproxBayesianRL.pdf | Dual Control for Approximate Bayesian Reinforcement Learning]] | ||

* Y. Pan and E.A. Theodorou, [[Media:DataDrivenDDPUsingGPs.pdf | Data-Driven Differential Dynamic Programming Using Gaussian Processes]] | * Y. Pan and E.A. Theodorou, [[Media:DataDrivenDDPUsingGPs.pdf | Data-Driven Differential Dynamic Programming Using Gaussian Processes]] | ||

+ | * F. Berkenkamp, R. Moriconi, A.P. Schoellig, A. Krause, [[Media:SafeLearningRegionsAttractionWithGPs.pdf | Safe Learning of Regions of Attraction for Uncertain, Nonlinear Systems with Gaussian Processes]] | ||

=== Web Links === | === Web Links === | ||

* [http://dsc.ijs.si/jus.kocijan/GPdyn/ Bibliography on GP Models in Dynamical Systems] | * [http://dsc.ijs.si/jus.kocijan/GPdyn/ Bibliography on GP Models in Dynamical Systems] |

## Revision as of 17:22, 13 October 2017

This page gathers references and materials related to the study of "Gaussian Process (GP) State Space Models (SSM)."

### Basic Gaussian Process Info

- Rasmussen and Williams

### Papers on GP-SSMs

- J.M. Wang, D.J. Fleet, A. Hertzmann, Gaussian Process Dynamical Models
- R. Turner, M.P. Deisenroth, C.E. Rasmussen, State-Space Inference and Learning with Gaussian Process;
- A. McHutchon, Nonlinear Modelling and Control Using Gaussian Processes (Ph.D. thesis, Cambridge University)
- J. Ko, D. Fox, GP-BayesFilters: Bayesian filtering using Gaussian Process Prediction and Observation Models
- F. Perez-Cruz, S.V. Vaerenbergh, J.J. Murrillo-Fuentes, M. Lazarro-Gredilla, and I. Santamaria, Gaussian Processes for Nonlinear Signal Processing;
- A. Svensson, A. Solin, S. Sarkka, T.B. Schon, Computationall Efficient Bayesian Learning of Gaussian Process State Space Models
- A.C. Damianou, M.K. Titsias, N.D. Lawrence, Variational Gaussian Process Dynamical Systems
- M.P. Deisenroth, D. Fox, C.E. Rasmussen, Gaussian Processes for Data-Efficient Learning in Robotics and Control;
- K. Jocikan, Dynamic GP Models: An Overview and Recent Developments;
- A. Solin, S. Sarkka, Hilbert Space Methods for Reduced-Rank Gaussian Process Regression; (ArXiv.1401.5508)
- C.L.C. Mattos, Z. Dai, A. Damianou, J. Forth, G.A. Barreto, N. Lawrence, Recorruent Gaussian Processes
- N.D. Lawrence, A.J. Moore, Hierarchical Gaussian Process Latent Variable Models
- M.K. Titsias, N.D. Lawrence, Bayesian Gaussian Process Latent Variable Model
- R. Calandra, J. Peters, C.E. Rasmussen, M.P. Deisenroth, Manifold Gaussian Processes for Regression
- F. Berkenkamp and A.P. Schoellig, Safe and Robust Learning Control with Gaussian Processes
- E.B. Fox, E.B. Sudderth, M.I. Jordan, A.S. Willsky, Sharing Features Among Dynamical Systems with Beta Processes
- J.M. Wang, D.j. Fleet, A. Hertzmann, Gaussian Process Dynamical Models for Human Motion
- E.D. Klenske, P. Hennig, Dual Control for Approximate Bayesian Reinforcement Learning
- Y. Pan and E.A. Theodorou, Data-Driven Differential Dynamic Programming Using Gaussian Processes
- F. Berkenkamp, R. Moriconi, A.P. Schoellig, A. Krause, Safe Learning of Regions of Attraction for Uncertain, Nonlinear Systems with Gaussian Processes