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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 331–340 of 1963 papers

TitleStatusHype
A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional DataCode0
Semi-parametric Expert Bayesian Network Learning with Gaussian Processes and Horseshoe Priors—0
Towards Improved Variational Inference for Deep Bayesian Models—0
Sparse discovery of differential equations based on multi-fidelity Gaussian process—0
Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes—0
Simulation Based Bayesian OptimizationCode0
Experimentally implemented dynamic optogenetic optimization of ATPase expression using knowledge-based and Gaussian-process-supported models—0
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage GuaranteesCode1
Data-Efficient Interactive Multi-Objective Optimization Using ParEGO—0
Information Flow Rate for Cross-Correlated Stochastic Processes—0
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Benchmark Results

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