SOTAVerified

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 21–30 of 1963 papers

TitleStatusHype
Unsupervised cell segmentation by fast Gaussian ProcessesCode0
Predictive posterior sampling from non-stationnary Gaussian process priors via Diffusion models with application to climate dataCode0
Multi-Output Gaussian Processes for Graph-Structured DataCode0
Gaussian Processes in Power Systems: Techniques, Applications, and Future Works—0
Modular Jump Gaussian ProcessesCode0
LLINBO: Trustworthy LLM-in-the-Loop Bayesian OptimizationCode1
Turbocharging Gaussian Process Inference with Approximate Sketch-and-ProjectCode0
Wasserstein Barycenter Gaussian Process based Bayesian Optimization—0
Modèles de Substitution pour les Modèles à base d'Agents : Enjeux, Méthodes et ApplicationsCode0
Integrative Analysis and Imputation of Multiple Data Streams via Deep Gaussian ProcessesCode0
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Benchmark Results

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