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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 611–620 of 1963 papers

TitleStatusHype
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal TransportCode0
Scalable Bayesian Transformed Gaussian Processes—0
Optimization on Manifolds via Graph Gaussian Processes—0
Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning—0
Locally Smoothed Gaussian Process Regression—0
Conditional Neural Processes for Molecules—0
Model of rough surfaces with Gaussian processes—0
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover TreesCode0
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes—0
Gaussian Processes on Distributions based on Regularized Optimal Transport—0
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Benchmark Results

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