SOTAVerified

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 301–310 of 1963 papers

TitleStatusHype
Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation—0
Interpolation pour l'augmentation de donnees : Application à la gestion des adventices de la canne a sucre a la Reunion—0
Multi-view Bayesian optimisation in reduced dimension for engineering design—0
Physics-informed Gaussian Processes for Safe Envelope ExpansionCode0
Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes—0
Scalable Bayesian Optimization via Focalized Sparse Gaussian ProcessesCode0
Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes—0
Bayesian Optimization of Bilevel Problems—0
Fast Multi-Group Gaussian Process Factor Models—0
Comparing noisy neural population dynamics using optimal transport distances—0
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Benchmark Results

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