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 521–530 of 1963 papers

TitleStatusHype
Longitudinal prediction of DNA methylation to forecast epigenetic outcomesCode0
Domain Invariant Learning for Gaussian Processes and Bayesian ExplorationCode0
Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes—0
Frequency-domain Gaussian Process Models for H_ Uncertainties—0
Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimation—0
Wiener Chaos in Kernel Regression: Towards Untangling Aleatoric and Epistemic Uncertainty—0
Sparse Variational Student-t Processes—0
Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes—0
Safe Stabilization with Model Uncertainties: A Universal Formula with Gaussian Process Learning—0
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes—0
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Benchmark Results

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