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 1661–1670 of 1963 papers

TitleStatusHype
Efficient Determination of Safety Requirements for Perception Systems—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT—0
Efficient Global Optimization using Deep Gaussian Processes—0
Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data—0
Efficiently Learning Nonstationary Gaussian Processes for Real World Impact—0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning—0
Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks—0
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Benchmark Results

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