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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 531–540 of 1963 papers

TitleStatusHype
Enhancing RSS-Based Visible Light Positioning by Optimal Calibrating the LED Tilt and Gain—0
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning—0
Evaluating Hospital Case Cost Prediction Models Using Azure Machine Learning Studio—0
Experimentally implemented dynamic optogenetic optimization of ATPase expression using knowledge-based and Gaussian-process-supported models—0
Towards Practical Lipschitz Bandits—0
Causal Inference using Gaussian Processes with Structured Latent Confounders—0
Approximation errors of online sparsification criteria—0
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning—0
Meta-models for transfer learning in source localisation—0
Approximation-Aware Bayesian Optimization—0
Show:102550
← PrevPage 54 of 197Next →

Benchmark Results

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