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 301–325 of 1963 papers

TitleStatusHype
Explainable Learning with Gaussian ProcessesCode0
A Gaussian Process-based Streaming Algorithm for Prediction of Time Series With Regimes and OutliersCode0
Exact Gaussian Processes on a Million Data PointsCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
Functional Regularisation for Continual Learning with Gaussian ProcessesCode0
Estimation of Dynamic Gaussian ProcessesCode0
A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process ModelCode0
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian ProcessesCode0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian ProcessesCode0
Estimating Latent Demand of Shared Mobility through Censored Gaussian ProcessesCode0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian ProcessesCode0
Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological imagesCode0
Entropic Trace Estimates for Log DeterminantsCode0
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEsCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
The Debiased Spatial Whittle LikelihoodCode0
Efficient Modeling of Latent Information in Supervised Learning using Gaussian ProcessesCode0
EigenGP: Gaussian Process Models with Adaptive EigenfunctionsCode0
Active Learning for Deep Gaussian Process SurrogatesCode0
Embarrassingly Parallel Inference for Gaussian ProcessesCode0
Efficient Inference in Multi-task Cox Process ModelsCode0
Show:102550
← PrevPage 13 of 79Next →

Benchmark Results

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