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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 1841–1850 of 1963 papers

TitleStatusHype
Fast Adaptive Weight Noise—0
Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations—0
Anomaly Detection and Removal Using Non-Stationary Gaussian Processes—0
MCMC for Variationally Sparse Gaussian Processes—0
Mondrian Forests for Large-Scale Regression when Uncertainty MattersCode0
Provable Bayesian Inference via Particle Mirror Descent—0
String Gaussian Process Kernels—0
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models—0
Optimal change point detection in Gaussian processes—0
Batch Bayesian Optimization via Local PenalizationCode0
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Benchmark Results

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