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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 1101–1110 of 1963 papers

TitleStatusHype
Sparse discovery of differential equations based on multi-fidelity Gaussian process—0
Sparse Gaussian Processes for Stochastic Differential Equations—0
Sparse Gaussian processes using pseudo-inputs—0
Sparse Gaussian Processes via Parametric Families of Compactly-supported Kernels—0
Sparse Gaussian Processes with Spherical Harmonic Features—0
Sparse Gaussian Processes with Spherical Harmonic Features Revisited—0
Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations—0
Sparse Kernel Gaussian Processes through Iterative Charted Refinement (ICR)—0
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning—0
Sparse Variational Contaminated Noise Gaussian Process Regression with Applications in Geomagnetic Perturbations Forecasting—0
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Benchmark Results

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