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gaussian process regression

133 papers

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Also known as

GPR GP REGRESSION

Co-occurring keywords

gaussian process (1200) bayesian inference (1907) uncertainty quantification (1393) variational inference (2069) kernel methods (1097) sparse approximation (76) sparse gaussian process (27) kernel approximation (100) hyperparameter optimization (406) online learning (1777)

Papers

Scalable Collaborative Bayesian Preference Learning AISTATS 2014
Incremental Local Gaussian Regression NIPS 2014
Random Walk Kernels and Learning Curves for Gaussian Process Regression on Random Graphs JMLR 2013
Efficient Optimization for Sparse Gaussian Process Regression NIPS 2013
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression NIPS 2013
A Framework for Evaluating Approximation Methods for Gaussian Process Regression JMLR 2013
Fast Near-GRID Gaussian Process Regression AISTATS 2013
Approximate Gaussian process inference for the drift function in stochastic differential equations NIPS 2013
Gaussian Kullback-Leibler Approximate Inference JMLR 2013
Infinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression AISTATS 2012
PAC-Bayesian Bound for Gaussian Process Regression and Multiple Kernel Additive Model COLT 2012
Real-Time Inverse Dynamics Learning for Musculoskeletal Robots based on Echo State Gaussian Process Regression RSS 2012
GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression JMLR 2012
Learning curves for multi-task Gaussian process regression NIPS 2012
Gaussian Process Training with Input Noise NIPS 2011
Domain Decomposition Approach for Fast Gaussian Process Regression of Large Spatial Data Sets JMLR 2011
Robust Gaussian Process Regression with a Student- Likelihood JMLR 2011
Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation NIPS 2010
Exact learning curves for Gaussian process regression on large random graphs NIPS 2010
Sparse Spectrum Gaussian Process Regression JMLR 2010
Efficient Non-Parametric Surface Representations Using Active Sampling for Push Broom Laser Data RSS 2010
Hierarchical Gaussian Process Regression ACML 2010
Kernels and learning curves for Gaussian process regression on random graphs NIPS 2009
Gaussian process regression with Student-t likelihood NIPS 2009
Lattice Regression NIPS 2009