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

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Showing 571–580 of 1963 papers

TitleStatusHype
lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal dataCode0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain DisturbancesCode0
Functional Regularisation for Continual Learning with Gaussian ProcessesCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable ModelsCode0
Federated Causal Inference from Observational DataCode0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
The Debiased Spatial Whittle LikelihoodCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
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Benchmark Results

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