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We propose a practical and scalable Gaussian process model for large-scale nonlinear probabilistic regression.
Adaptive Mixtures of Local Experts
R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton · 1991
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Efficient Global Optimization of Expensive Black-Box Functions
D. R. Jones, M. Schonlau, and W. J. Welch · 1998
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A Bayesian Committee Machine
V. Tresp · 2000
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Using the Nyström Method to Speed up Kernel Machines
C. K. Williams and M. Seeger · 2001
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Infinite Mixtures of Gaussian Process Experts
C. E. Rasmussen and Z. Ghahramani · 2002
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Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
N. Lawrence · 2005
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A Unifying View of Sparse Approximate Gaussian Process Regression
J. Quiñonero-Candela and C. E. Rasmussen · 2005
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An Alternative Infinite Mixture of Gaussian Process Experts
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Gaussian Processes for Machine Learning
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Fast Gaussian Process Regression Using KD-Trees
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Sparse Gaussian Processes using Pseudo-inputs
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Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies
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A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
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Variational Mixture of Gaussian Process Experts
C. Yuan and C. Neubauer · 2009
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Sparse Spectrum Gaussian Process Regression
M. Lázaro-Gredilla, J. Quiñonero-Candela, C. E. Rasmussen, and A. R. Figueiras-Vidal · 2010
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Efficient Gaussian Process Inference for Short-Scale Spatio-Temporal Modeling
J. Luttinen and A. Ilin · 2012
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Deep Gaussian Processes
A. Damianou and N. D. Lawrence · 2013
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Gaussian Processes for Big Data
J. Hensman, N. Fusi, and N. D. Lawrence · 2013
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Gaussian Processes for Data-Efficient Learning in Robotics and Control
M. P. Deisenroth, D. Fox, and C. E. Rasmussen · 2014
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Model Learning with Local Gaussian Process Regression
D. Nguyen-Tuong, M. Seeger, and J. Peters · 2009
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Variational Learning of Inducing Variables in Sparse Gaussian Processes
M. K. Titsias · 2009
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Kin Family of Datasets
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Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
Y. Gal, M. van der Wilk, and C. E. Rasmussen · 2014
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