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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 691–700 of 1963 papers

TitleStatusHype
Frequency-domain Gaussian Process Models for H_ Uncertainties—0
Composite likelihood estimation of stationary Gaussian processes with a view toward stochastic volatility—0
Compositionally-Warped Gaussian Processes—0
From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery—0
A Perspective on Gaussian Processes for Earth Observation—0
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations—0
Data-Driven Abstractions via Binary-Tree Gaussian Processes for Formal Verification—0
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning—0
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
Bayesian Variational Optimization for Combinatorial Spaces—0
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Benchmark Results

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