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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 671–680 of 1963 papers

TitleStatusHype
Gaussian Experts Selection using Graphical Models—0
Bayesian Warped Gaussian Processes—0
Physics Enhanced Data-Driven Models with Variational Gaussian Processes—0
Combining Parametric Land Surface Models with Machine Learning—0
Few-shot Learning for Spatial Regression—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
Financial Applications of Gaussian Processes and Bayesian Optimization—0
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data—0
Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data—0
Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety—0
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Benchmark Results

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