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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 91–100 of 1963 papers

TitleStatusHype
Supervising the Multi-Fidelity Race of Hyperparameter ConfigurationsCode1
Efficiently Sampling Functions from Gaussian Process PosteriorsCode1
Exact, Fast and Expressive Poisson Point Processes via Squared Neural FamiliesCode1
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
A Unifying Variational Framework for Gaussian Process Motion PlanningCode1
Gaussian Processes for Missing Value ImputationCode1
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy dataCode1
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processesCode1
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black HolesCode1
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Benchmark Results

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