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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 911–920 of 1963 papers

TitleStatusHype
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes—0
Deep Gaussian Processes for Regression using Approximate Expectation Propagation—0
Incremental Ensemble Gaussian Processes—0
Incremental Learning of Motion Primitives for Pedestrian Trajectory Prediction at Intersections—0
Incremental Structure Discovery of Classification via Sequential Monte Carlo—0
Index Set Fourier Series Features for Approximating Multi-dimensional Periodic Kernels—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Inducing Gaussian Process Networks—0
Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes—0
Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables—0
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Benchmark Results

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