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 626–650 of 1963 papers

TitleStatusHype
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes—0
Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Use—0
Experimentally implemented dynamic optogenetic optimization of ATPase expression using knowledge-based and Gaussian-process-supported models—0
Functional Gaussian processes for regression with linear PDE models—0
Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification—0
Gaussian Process Accelerated Feldman-Cousins Approach for Physical Parameter Inference—0
Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks—0
BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems—0
Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature—0
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning—0
A Driver Behavior Modeling Structure Based on Non-parametric Bayesian Stochastic Hybrid Architecture—0
Exponentially Stable Projector-based Control of Lagrangian Systems with Gaussian Processes—0
Extended and Unscented Gaussian Processes—0
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
Functional Priors for Bayesian Neural Networks through Wasserstein Distance Minimization to Gaussian Processes—0
Extrinsic Bayesian Optimizations on Manifolds—0
Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes—0
Facility Deployment Decisions through Warp Optimizaton of Regressed Gaussian Processes—0
Fairness-aware Bayes optimal functional classification—0
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning—0
Fast Adaptation with Linearized Neural Networks—0
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes—0
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning—0
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes—0
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations—0
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Benchmark Results

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