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 1571–1580 of 1963 papers

TitleStatusHype
On Lebesgue Integral Quadrature—0
Mixed-Stationary Gaussian Process for Flexible Non-Stationary Modeling of Spatial Outcomes—0
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient MatchingCode0
A Driver Behavior Modeling Structure Based on Non-parametric Bayesian Stochastic Hybrid Architecture—0
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning—0
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences—0
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)—0
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm DesignCode0
Probabilistic Bisection with Spatial Metamodels—0
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Benchmark Results

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