SOTAVerified

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 1861–1870 of 1963 papers

TitleStatusHype
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)—0
Gaussian Process Pseudo-Likelihood Models for Sequence Labeling—0
Regression with Linear Factored Functions—0
Nested Variational Compression in Deep Gaussian Processes—0
Probabilistic Differential Dynamic Programming—0
Analysis of Brain States from Multi-Region LFP Time-Series—0
Extended and Unscented Gaussian Processes—0
Fast Kernel Learning for Multidimensional Pattern Extrapolation—0
Two Gaussian Approaches to Black-Box Optomization—0
A chain rule for the expected suprema of Gaussian processes—0
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Benchmark Results

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