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 1121–1130 of 1963 papers

TitleStatusHype
Fast Gaussian Processes under Monotonicity Constraints—0
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation—0
Fast Gaussian Process Regression for Big Data—0
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
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Benchmark Results

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