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 751–760 of 1963 papers

TitleStatusHype
Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian ProcessesCode0
Inference on Causal Effects of Interventions in Time using Gaussian Processes—0
Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing EnsemblesCode0
Active Learning for Regression with Aggregated Outputs—0
Log-Linear-Time Gaussian Processes Using Binary Tree KernelsCode0
Safety-Aware Learning-Based Control of Systems with Uncertainty Dependent Constraints (extended version)—0
Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding—0
Physically Meaningful Uncertainty Quantification in Probabilistic Wind Turbine Power Curve Models as a Damage Sensitive Feature—0
Optimal Stopping with Gaussian Processes—0
Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularization—0
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Benchmark Results

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