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 1731–1740 of 1963 papers

TitleStatusHype
Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes—0
Fast Kernel Learning for Multidimensional Pattern Extrapolation—0
Fast methods for training Gaussian processes on large data sets—0
Fast Multi-Group Gaussian Process Factor Models—0
Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes—0
Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes—0
Few-shot Learning for Spatial Regression—0
Financial Applications of Gaussian Processes and Bayesian Optimization—0
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection—0
Finite sample approximations of exact and entropic Wasserstein distances between covariance operators and Gaussian processes—0
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Benchmark Results

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