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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 751–760 of 1963 papers

TitleStatusHype
Adaptive Cholesky Gaussian ProcessesCode0
Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces—0
Invariance Learning in Deep Neural Networks with Differentiable Laplace ApproximationsCode1
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes—0
Supervising the Multi-Fidelity Race of Hyperparameter ConfigurationsCode1
Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures—0
A Statistical Learning View of Simple KrigingCode0
Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes—0
The Schrödinger Bridge between Gaussian Measures has a Closed Form—0
Multi-model Ensemble Analysis with Neural Network Gaussian Processes—0
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Benchmark Results

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