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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 1251–1260 of 1963 papers

TitleStatusHype
Value-at-Risk Optimization with Gaussian Processes—0
Variable noise and dimensionality reduction for sparse Gaussian processes—0
Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes—0
Variance-Reducing Couplings for Random Features—0
Variance Reduction for Matrix Computations with Applications to Gaussian Processes—0
Variational Auto-encoded Deep Gaussian Processes—0
Black-Box Inference for Non-Linear Latent Force Models—0
Variational Calibration of Computer Models—0
Variational Elliptical Processes—0
Variational Gaussian Processes: A Functional Analysis View—0
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Benchmark Results

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