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Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

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Showing 1921–1930 of 1963 papers

TitleStatusHype
A dependent partition-valued process for multitask clustering and time evolving network modelling—0
Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes—0
Gaussian Processes for Nonlinear Signal Processing—0
Gaussian Process Kernels for Pattern Discovery and ExtrapolationCode0
Multiresolution Gaussian Processes—0
Bayesian Warped Gaussian Processes—0
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression—0
Collaborative Gaussian Processes for Preference Learning—0
Random walk kernels and learning curves for Gaussian process regression on random graphs—0
Deep Gaussian Processes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified