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 231–240 of 1963 papers

TitleStatusHype
Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning ApproachCode0
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian ProcessesCode0
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes—0
Physically Consistent Modeling & Identification of Nonlinear Friction with Dissipative Gaussian Processes—0
Gradients of Functions of Large MatricesCode0
Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction—0
Variance-Reducing Couplings for Random Features—0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Minimizing UCB: a Better Local Search Strategy in Local Bayesian Optimization—0
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial DataCode0
Show:102550
← PrevPage 24 of 197Next →

Benchmark Results

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