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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 221–230 of 1963 papers

TitleStatusHype
Modular Jump Gaussian ProcessesCode0
Gaussian Processes in Power Systems: Techniques, Applications, and Future Works—0
Turbocharging Gaussian Process Inference with Approximate Sketch-and-ProjectCode0
Wasserstein Barycenter Gaussian Process based Bayesian Optimization—0
Modèles de Substitution pour les Modèles à base d'Agents : Enjeux, Méthodes et ApplicationsCode0
Integrative Analysis and Imputation of Multiple Data Streams via Deep Gaussian ProcessesCode0
STRIDE: Sparse Techniques for Regression in Deep Gaussian Processes—0
Convergence Rates of Constrained Expected Improvement—0
A Fast Kernel-based Conditional Independence test with Application to Causal Discovery—0
Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations—0
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Benchmark Results

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