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 1941–1950 of 1963 papers

TitleStatusHype
Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomes—0
Kalman Filtering with Gaussian Processes Measurement Noise—0
Kernel Conditional Density Operators—0
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness—0
Kernel Distillation for Fast Gaussian Processes Prediction—0
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression—0
Knot Selection in Sparse Gaussian Processes—0
Know Thy Student: Interactive Learning with Gaussian Processes—0
Koopman-Equivariant Gaussian Processes—0
Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation—0
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Benchmark Results

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