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 1641–1650 of 1963 papers

TitleStatusHype
Cooperative Online Learning for Multi-Agent System Control via Gaussian Processes with Event-Triggered Mechanism: Extended Version—0
Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes—0
Distributed Gaussian Process Based Cooperative Visual Pursuit Control for Drone Networks—0
Distributed Gaussian Processes—0
Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes—0
Distributed non-parametric deep and wide networks—0
Distributional Gaussian Processes Layers for Out-of-Distribution Detection—0
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation—0
Distributionally Robust Model-based Reinforcement Learning with Large State Spaces—0
Distributionally Robust Model Predictive Control with Mixture of Gaussian Processes—0
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Benchmark Results

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