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 631–640 of 1963 papers

TitleStatusHype
BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems—0
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration—0
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning—0
Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature—0
A Driver Behavior Modeling Structure Based on Non-parametric Bayesian Stochastic Hybrid Architecture—0
Forecasting intermittent time series with Gaussian Processes and Tweedie likelihood—0
Exponentially Stable Projector-based Control of Lagrangian Systems with Gaussian Processes—0
Extended and Unscented Gaussian Processes—0
Fractional Barndorff-Nielsen and Shephard model: applications in variance and volatility swaps, and hedging—0
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations—0
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Benchmark Results

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