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 261–270 of 1963 papers

TitleStatusHype
Autoencoder Attractors for Uncertainty EstimationCode0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
Entropic Trace Estimates for Log DeterminantsCode0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Estimating Latent Demand of Shared Mobility through Censored Gaussian ProcessesCode0
EigenGP: Gaussian Process Models with Adaptive EigenfunctionsCode0
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation PropagationCode0
Show:102550
← PrevPage 27 of 197Next →

Benchmark Results

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