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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 1751–1760 of 1963 papers

TitleStatusHype
Environmental Modeling Framework using Stacked Gaussian Processes—0
Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes—0
Variational Fourier features for Gaussian processesCode0
The Recycling Gibbs Sampler for Efficient Learning—0
Faster variational inducing input Gaussian process classification—0
Gaussian Processes for Survival Analysis—0
Stochastic Variational Deep Kernel Learning—0
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes—0
Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes—0
GPflow: A Gaussian process library using TensorFlowCode2
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Benchmark Results

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