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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1101–1110 of 1963 papers

TitleStatusHype
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models—0
Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature—0
Exponentially Stable Projector-based Control of Lagrangian Systems with Gaussian Processes—0
Extended and Unscented Gaussian Processes—0
Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice—0
Extracting Predictive Information from Heterogeneous Data Streams using Gaussian Processes—0
Extrinsic Bayesian Optimizations on Manifolds—0
Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes—0
Facility Deployment Decisions through Warp Optimizaton of Regressed Gaussian Processes—0
Fairness-aware Bayes optimal functional classification—0
Show:102550
← PrevPage 111 of 197Next →

Benchmark Results

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