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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 1941–1950 of 1963 papers

TitleStatusHype
Optimal Reinforcement Learning for Gaussian Systems—0
Nonlinear Inverse Reinforcement Learning with Gaussian ProcessesCode0
Gaussian Process Regression NetworksCode0
Overlapping Mixtures of Gaussian Processes for the Data Association Problem—0
Linear Latent Force Models using Gaussian Processes—0
Kernels for Vector-Valued Functions: a ReviewCode0
Evaluation of Rarity of Fingerprints in Forensics—0
Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors—0
Approximate inference in continuous time Gaussian-Jump processes—0
Heavy-Tailed Process Priors for Selective Shrinkage—0
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Benchmark Results

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