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 1471–1480 of 1963 papers

TitleStatusHype
Bayesian model selection consistency and oracle inequality with intractable marginal likelihood—0
Bayesian Multi-Scale Optimistic Optimization—0
Bayesian neural network unit priors and generalized Weibull-tail property—0
Bayesian Optimisation with Gaussian Processes for Premise Selection—0
Bayesian Optimization Assisted Meal Bolus Decision Based on Gaussian Processes Learning and Risk-Sensitive Control—0
Bayesian Optimization by Kernel Regression and Density-based Exploration—0
Bayesian optimization explains human active search—0
Bayesian Optimization of Bilevel Problems—0
Bayesian optimization of distributed neurodynamical controller models for spatial navigation—0
Bayesian Optimization via Continual Variational Last Layer Training—0
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Benchmark Results

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