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 1551–1560 of 1963 papers

TitleStatusHype
Control Barrier Functions for Unknown Nonlinear Systems using Gaussian Processes—0
Controllable Expensive Multi-objective Learning with Warm-starting Bayesian Optimization—0
Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization—0
Convergence and Concentration of Empirical Measures under Wasserstein Distance in Unbounded Functional Spaces—0
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness—0
Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions—0
Convolutional Normalizing Flows for Deep Gaussian Processes—0
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies—0
Correcting Model Bias with Sparse Implicit Processes—0
Correlated Product of Experts for Sparse Gaussian Process Regression—0
Show:102550
← PrevPage 156 of 197Next →

Benchmark Results

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