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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 1651–1660 of 1963 papers

TitleStatusHype
Distributed non-parametric deep and wide networks—0
Sparse Covariance Modeling in High Dimensions with Gaussian Processes—0
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distributionCode0
Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data—0
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes—0
Scalable Levy Process Priors for Spectral Kernel Learning—0
Gaussian process based nonlinear latent structure discovery in multivariate spike train data—0
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models—0
Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease ProgressionCode0
Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian ProcessesCode0
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Benchmark Results

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