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 971–980 of 1963 papers

TitleStatusHype
Gaussian Process Latent Class Choice Models—0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
Convergence and Concentration of Empirical Measures under Wasserstein Distance in Unbounded Functional Spaces—0
Is SGD a Bayesian sampler? Well, almost—0
Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization—0
Attentive Gaussian processes for probabilistic time-series generation—0
A General Framework for Fair Regression—0
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes—0
STRIDE: Sparse Techniques for Regression in Deep Gaussian Processes—0
DKL-KAN: Scalable Deep Kernel Learning using Kolmogorov-Arnold Networks—0
Show:102550
← PrevPage 98 of 197Next →

Benchmark Results

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