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 361–370 of 1963 papers

TitleStatusHype
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes—0
A Robust Asymmetric Kernel Function for Bayesian Optimization, with Application to Image Defect Detection in Manufacturing Systems—0
Conformal Prediction for Manifold-based Source Localization with Gaussian Processes—0
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes—0
A Statistical Machine Learning Approach to Yield Curve Forecasting—0
Consistency of some sequential experimental design strategies for excursion set estimation based on vector-valued Gaussian processes—0
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck—0
Optimal Privacy-Aware Stochastic Sampling—0
Constrained Bayesian Optimization under Bivariate Gaussian Process with Application to Cure Process Optimization—0
A Fully-Automated Framework Integrating Gaussian Process Regression and Bayesian Optimization to Design Pin-Fins—0
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Benchmark Results

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