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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 1391–1400 of 1963 papers

TitleStatusHype
Approximate Bayesian Optimisation for Neural Networks—0
Approximate Bayes learning of stochastic differential equations—0
Approximate inference in continuous time Gaussian-Jump processes—0
Approximate Sampling using an Accelerated Metropolis-Hastings based on Bayesian Optimization and Gaussian Processes—0
The Past Does Matter: Correlation of Subsequent States in Trajectory Predictions of Gaussian Process Models—0
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC—0
Approximation-Aware Bayesian Optimization—0
Approximation errors of online sparsification criteria—0
Towards Practical Lipschitz Bandits—0
A Practitioner's Guide to Automatic Kernel Search for Gaussian Processes in Battery Applications—0
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Benchmark Results

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