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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 351–360 of 1963 papers

TitleStatusHype
Entropic Trace Estimates for Log DeterminantsCode0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Gaussian Process Behaviour in Wide Deep Neural NetworksCode0
A Gaussian Process-based Streaming Algorithm for Prediction of Time Series With Regimes and OutliersCode0
Conditionally Independent Multiresolution Gaussian ProcessesCode0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
The Debiased Spatial Whittle LikelihoodCode0
Efficient Modeling of Latent Information in Supervised Learning using Gaussian ProcessesCode0
EigenGP: Gaussian Process Models with Adaptive EigenfunctionsCode0
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Benchmark Results

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