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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 271–280 of 1963 papers

TitleStatusHype
Entropic Trace Estimates for Log DeterminantsCode0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
Active Learning with Gaussian Processes for High Throughput PhenotypingCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
The Debiased Spatial Whittle LikelihoodCode0
EigenGP: Gaussian Process Models with Adaptive EigenfunctionsCode0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
Efficient Modeling of Latent Information in Supervised Learning using Gaussian ProcessesCode0
Embarrassingly Parallel Inference for Gaussian ProcessesCode0
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Benchmark Results

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