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 1481–1490 of 1963 papers

TitleStatusHype
Bayesian Optimization with Informative Covariance—0
Bayesian Optimization with Tree-structured Dependencies—0
Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers—0
Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables—0
Bayesian Quantile and Expectile Optimisation—0
Bayesian Relational Generative Model for Scalable Multi-modal Learning—0
Bayesian Sparse Factor Analysis with Kernelized Observations—0
Bayesian Variational Optimization for Combinatorial Spaces—0
Bayesian Warped Gaussian Processes—0
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction—0
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Benchmark Results

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