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gaussian process

1200 papers

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Also known as

GP

Co-occurring keywords

variational inference (2069) bayesian optimization (624) bayesian inference (1907) uncertainty quantification (1393) kernel methods (1097) gaussian processes (89) regret bound (1926) active learning (1301) sparse approximation (76) hyperparameter optimization (406)

Papers

Bayesian estimation of orientation preference maps NIPS 2009
Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes NIPS 2009
Stable and Efficient Gaussian Process Calculations JMLR 2009
Gaussian process regression with Student-t likelihood NIPS 2009
Kernels and learning curves for Gaussian process regression on random graphs NIPS 2009
Learning GP-BayesFilters via Gaussian process latent variable models RSS 2009
Variational Mixture of Gaussian Process Experts NIPS 2008
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity NIPS 2008
Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes NIPS 2008
Approximations for Binary Gaussian Process Classification JMLR 2008
Sparse Convolved Gaussian Processes for Multi-output Regression NIPS 2008
Gas Distribution Modeling using Sparse Gaussian Process Mixture Models RSS 2008
Modeling human function learning with Gaussian processes NIPS 2008
Bayesian Kernel Shaping for Learning Control NIPS 2008
Adapting to a Market Shock: Optimal Sequential Market-Making NIPS 2008
Multi-task Gaussian Process Learning of Robot Inverse Dynamics NIPS 2008
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes NIPS 2008
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies JMLR 2008
Efficient Sampling for Gaussian Process Inference using Control Variables NIPS 2008
The Gaussian Process Density Sampler NIPS 2008
Multi-task Gaussian Process Prediction NIPS 2007
Bayesian Co-Training NIPS 2007
Bundle Methods for Machine Learning NIPS 2007
Robust Regression with Twinned Gaussian Processes NIPS 2007
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes NIPS 2007