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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 421–430 of 1963 papers

TitleStatusHype
Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables—0
Data-driven Bayesian Control of Port-Hamiltonian Systems—0
A computationally lightweight safe learning algorithm—0
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear AlgebraCode2
Distributionally Robust Model-based Reinforcement Learning with Large State Spaces—0
Les Houches Lectures on Deep Learning at Large & Infinite Width—0
Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space ModelsCode1
A Unifying Variational Framework for Gaussian Process Motion PlanningCode1
Latent Variable Multi-output Gaussian Processes for Hierarchical DatasetsCode0
Heterogeneous Multi-Task Gaussian Cox ProcessesCode0
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Benchmark Results

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