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 1126–1150 of 1963 papers

TitleStatusHype
Compressible Spectral Mixture Kernels with Sparse Dependency Structures for Gaussian Processes—0
Spectrum Gaussian Processes Based On Tunable Basis Functions—0
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes—0
Spike and Slab Gaussian Process Latent Variable Models—0
Splitting Gaussian Process Regression for Streaming Data—0
srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications—0
Stable spline identification of linear systems under missing data—0
STACI: Spatio-Temporal Aleatoric Conformal Inference—0
Stagewise Safe Bayesian Optimization with Gaussian Processes—0
State Space Gaussian Processes with Non-Gaussian Likelihood—0
State Space representation of non-stationary Gaussian Processes—0
Stationarity without mean reversion in improper Gaussian processes—0
Statistical abstraction for multi-scale spatio-temporal systems—0
Statistical Analysis of the LMS Algorithm for Proper and Improper Gaussian Processes—0
Statistical Deep Learning for Spatial and Spatio-Temporal Data—0
Steps Toward Deep Kernel Methods from Infinite Neural Networks—0
Stochastic data-driven model predictive control using Gaussian processes—0
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes—0
Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory—0
Stochastic Model Predictive Control Utilizing Bayesian Neural Networks—0
Stochastic MPC for energy hubs using data driven demand forecasting—0
Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes—0
Stochastic Portfolio Theory: A Machine Learning Perspective—0
Stochastic Process Bandits: Upper Confidence Bounds Algorithms via Generic Chaining—0
Stochastic Variational Deep Kernel Learning—0
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Benchmark Results

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