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 921–930 of 1963 papers

TitleStatusHype
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
A Novel Gaussian Min-Max Theorem and its Applications—0
Inference for Large Scale Regression Models with Dependent Errors—0
Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An application to high-throughput toxicity testing—0
Inference on Causal Effects of Interventions in Time using Gaussian Processes—0
Dynamic Term Structure Models with Nonlinearities using Gaussian Processes—0
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes—0
Inferring power system dynamics from synchrophasor data using Gaussian processes—0
Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers—0
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Benchmark Results

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