GP SSM
From Robotics
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
