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 1611–1620 of 1963 papers

TitleStatusHype
Asymmetric kernel in Gaussian Processes for learning target variance—0
Gaussian Processes indexed on the symmetric group: prediction and learning—0
Constant-Time Predictive Distributions for Gaussian ProcessesCode0
Gaussian Processes Over Graphs—0
Variational zero-inflated Gaussian processes with sparse kernelsCode0
Learning unknown ODE models with Gaussian processesCode0
Dimension-Robust MCMC in Bayesian Inverse Problems—0
Conditionally Independent Multiresolution Gaussian ProcessesCode0
Product Kernel Interpolation for Scalable Gaussian Processes—0
Machine learning based hyperspectral image analysis: A survey—0
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Benchmark Results

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