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 81–90 of 1963 papers

TitleStatusHype
New Bounds for Sparse Variational Gaussian Processes—0
Recurrent Memory for Online Interdomain Gaussian Processes—0
Bayesian Optimization by Kernel Regression and Density-based Exploration—0
Koopman-Equivariant Gaussian Processes—0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
Distributionally Robust Model Predictive Control with Mixture of Gaussian Processes—0
Decentralized Online Ensembles of Gaussian Processes for Multi-Agent SystemsCode0
Tighter sparse variational Gaussian processes—0
Gaussian Process Regression for Inverse Problems in Linear PDEs—0
Student-t processes as infinite-width limits of posterior Bayesian neural networks—0
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Benchmark Results

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