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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 1361–1370 of 1963 papers

TitleStatusHype
Kalman Filtering with Gaussian Processes Measurement Noise—0
Differentially Private Regression and Classification with Sparse Gaussian Processes—0
Bayesian Optimisation with Gaussian Processes for Premise Selection—0
No-Regret Learning in Unknown Games with Correlated PayoffsCode0
Compositional uncertainty in deep Gaussian processesCode0
Band-Limited Gaussian Processes: The Sinc Kernel—0
Coarse-scale PDEs from fine-scale observations via machine learning—0
Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version—0
Finite size corrections for neural network Gaussian processes—0
Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal DynamicsCode0
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Benchmark Results

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