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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 1011–1020 of 1963 papers

TitleStatusHype
Robust Bayesian Optimization with Student-t Likelihood—0
Robust Bayesian Target Value Optimization—0
Robust Deep Gaussian Processes—0
Robust Filtering and Smoothing with Gaussian Processes—0
Robust Gaussian Processes via Relevance Pursuit—0
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes—0
Dimension-Robust MCMC in Bayesian Inverse Problems—0
Robustness bounds on the successful adversarial examples in probabilistic models: Implications from Gaussian processes—0
Robust Optimization with Diffusion Models for Green Security—0
Robust Regression with Twinned Gaussian Processes—0
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Benchmark Results

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