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 1171–1180 of 1963 papers

TitleStatusHype
Multi-fidelity modeling with different input domain definitions using Deep Gaussian Processes—0
Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian ProcessesCode1
Is SGD a Bayesian sampler? Well, almost—0
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization—0
Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry—0
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation—0
Beyond Grids: Multi-objective Bayesian Optimization With Adaptive DiscretizationCode0
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian ProcessesCode1
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Likelihood-Free Inference with Deep Gaussian ProcessesCode0
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Benchmark Results

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