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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 421–430 of 1963 papers

TitleStatusHype
A Learnable Safety MeasureCode0
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian ProcessesCode0
Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian ProcessesCode0
Automatic Construction and Natural-Language Description of Nonparametric Regression ModelsCode0
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian ProcessesCode0
Approximate Latent Force Model InferenceCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Decomposing Gaussians with Unknown CovarianceCode0
Boundary Exploration for Bayesian Optimization With Unknown Physical ConstraintsCode0
Approximate Inference Turns Deep Networks into Gaussian ProcessesCode0
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Benchmark Results

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