GP SSM

This page gathers references and materials related to the study of

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

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
• 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

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• 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.

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.

Web Links

• Bibliography on GP Models in Dynamical Systems