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 1021–1030 of 1963 papers

TitleStatusHype
Gaussian Process Latent Class Choice Models—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
Damage detection in operational wind turbine blades using a new approach based on machine learning—0
Model-based Policy Search for Partially Measurable Systems—0
Convolutional conditional neural processes for local climate downscalingCode1
Bayesian Optimization Assisted Meal Bolus Decision Based on Gaussian Processes Learning and Risk-Sensitive Control—0
A Renormalization Group Approach to Connect Discrete- and Continuous-Time Descriptions of Gaussian Processes—0
Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control—0
Improved active output selection strategy for noisy environments—0
Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models—0
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Benchmark Results

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