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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 1341–1350 of 1963 papers

TitleStatusHype
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options—0
A Gaussian process latent force model for joint input-state estimation in linear structural systems—0
A Gaussian Process Model for Ordinal Data with Applications to Chemoinformatics—0
Correlated Dynamics in Marketing Sensitivities—0
A Gaussian Process perspective on Convolutional Neural Networks—0
A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking—0
A Gaussian Process Regression Model for Distribution Inputs—0
A General Framework for Fair Regression—0
A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes—0
A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes—0
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Benchmark Results

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