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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 811–820 of 1963 papers

TitleStatusHype
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes—0
Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data—0
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization—0
Global optimization using Gaussian Processes to estimate biological parameters from image data—0
A Perspective on Gaussian Processes for Earth Observation—0
Bayesian Variational Optimization for Combinatorial Spaces—0
Data-driven Bayesian Control of Port-Hamiltonian Systems—0
GP3: A Sampling-based Analysis Framework for Gaussian Processes—0
A computationally lightweight safe learning algorithm—0
Grouped Gaussian Processes for Solar Power Prediction—0
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Benchmark Results

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