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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 731–740 of 1963 papers

TitleStatusHype
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO AlgorithmCode0
On power sum kernels on symmetric groups—0
Multi-output Gaussian processes for inverse uncertainty quantification in neutron noise analysis—0
Fully Bayesian inference for latent variable Gaussian process modelsCode0
Black-box Coreset Variational InferenceCode0
Isotropic Gaussian Processes on Finite Spaces of GraphsCode0
Benefits of Monotonicity in Safe Exploration with Gaussian ProcessesCode0
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning—0
Monte Carlo Tree Descent for Black-Box Optimization—0
Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models—0
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Benchmark Results

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