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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 721–730 of 1963 papers

TitleStatusHype
Challenges in Gaussian Processes for Non Intrusive Load MonitoringCode0
Introduction and Exemplars of Uncertainty Decomposition—0
Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification—0
Neural Inference of Gaussian Processes for Time Series Data of QuasarsCode0
Global Optimization with Parametric Function Approximation—0
Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes—0
Provably Reliable Large-Scale Sampling from Gaussian ProcessesCode0
Learning Neural Optimal Interpolation Models and Solvers—0
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice—0
Towards Improved Learning in Gaussian Processes: The Best of Two Worlds—0
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Benchmark Results

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