# Difference between revisions of "GP SSM"

From Robotics

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* Deep Learning | * Deep Learning | ||

* Koopman Spectral Methods. | * Koopman Spectral Methods. | ||

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+ | == Gaussian Process Approaches == | ||

=== Basic Gaussian Process Info === | === Basic Gaussian Process Info === | ||

* Rasmussen and Williams | * Rasmussen and Williams | ||

+ | |||

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

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

=== Papers on GP-SSMs === | === Papers on GP-SSMs === | ||

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* J. Umlauft, T. Beckers, M. Kimmel, S. Hirsche, [[Media:FeedbackLinearlizatingUsingGPs.pdf | Feedback Linearization Using Gaussian Processes]] | * J. Umlauft, T. Beckers, M. Kimmel, S. Hirsche, [[Media:FeedbackLinearlizatingUsingGPs.pdf | Feedback Linearization Using Gaussian Processes]] | ||

* F. Lindsten, M.I. Jordan, T.B. Schon, [[Media:ParticleGibbsWithAncestorSamping.pdf | Particles Gibbs with Ancestor Sampling]], ''J. Machine Learning Research'', vo. 15, pp. 2145-2184. | * F. Lindsten, M.I. Jordan, T.B. Schon, [[Media:ParticleGibbsWithAncestorSamping.pdf | Particles Gibbs with Ancestor Sampling]], ''J. Machine Learning Research'', vo. 15, pp. 2145-2184. | ||

+ | |||

+ | == Deep Learning == | ||

+ | |||

+ | === Papers on Deep Learning === | ||

+ | * Soatto Paper | ||

+ | |||

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

+ | |||

+ | == Koopman Spectral Method == | ||

=== Papers on Koopman Spectral methods === | === Papers on Koopman Spectral methods === | ||

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* Papers which focus on fluids | * Papers which focus on fluids | ||

** I. Mezic, [[Media:AnalysisFluidFlows.pdf | Analysis of Fluid Flows via Spectral Properties of the Koopman Operator]], ''Annual Review of Fluids,'' vol. 45, 357-378, 2013. | ** I. Mezic, [[Media:AnalysisFluidFlows.pdf | Analysis of Fluid Flows via Spectral Properties of the Koopman Operator]], ''Annual Review of Fluids,'' vol. 45, 357-378, 2013. | ||

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## Revision as of 00:18, 9 January 2018

This page gathers references and materials related to the study of

- Gaussian Process (GP) State Space Models (SSM)
- Deep Learning
- Koopman Spectral Methods.

## Contents

## Gaussian Process Approaches

### Basic Gaussian Process Info

- Rasmussen and Williams

### Web Links

### 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
- M.P. Deisenroth, J. Peters, C.E. Rasmussen, Approximate Dynamic Programming with Gaussian Processes
- R. Frigola, F. Lindsten, T.B. Schon, C.E. Rasmussen, Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM

- T. Beckers, J. Umlauft, and S. Hirsche, Stable Model-Based Control with Gaussian Process Regression for Robot Manipulators,
- A. Marco, P. Hennig, S. Schaal, S. Trimpe, On the Design of LQR Kernels for Efficient Controller Learning,
*arXiv:1709.07089v1* - N. Gorbach, S. Bauer, J. Buhmann, Scalable Variational Inference for Dynamical Systems, NIPS 2017, Long Beach, CA, 2017.
- J. Umlauft, T. Beckers, M. Kimmel, S. Hirsche, Feedback Linearization Using Gaussian Processes
- F. Lindsten, M.I. Jordan, T.B. Schon, Particles Gibbs with Ancestor Sampling,
*J. Machine Learning Research*, vo. 15, pp. 2145-2184.

## Deep Learning

### Papers on Deep Learning

- Soatto Paper

### Web Links

## Koopman Spectral Method

### Papers on Koopman Spectral methods

- S. Brunton, J. Proctor, N. Kutz, Discovering Governing Equations from Data: Sparse Identification of Nonlinear Dyanmical Systems,
*arXiv:1509.03580v1* - M. Budisic, R. Mohr, I. Mezic, Applied Koopmanism,
*Chaos*, vol. 22, 2012. - J.L. Proctor, S.L. Brunton, J.N. Kutz, Dynamic Mode Decomposition with Control, SIAM J. Applied Dynamical Systems, vol. 15, no. 1, pp. 142-161, 2016.
- Papers which focus on fluids
- I. Mezic, Analysis of Fluid Flows via Spectral Properties of the Koopman Operator,
*Annual Review of Fluids,*vol. 45, 357-378, 2013.

- I. Mezic, Analysis of Fluid Flows via Spectral Properties of the Koopman Operator,