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 1221–1230 of 1963 papers

TitleStatusHype
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian ProcessesCode0
Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization—0
Upper Trust Bound Feasibility Criterion for Mixed Constrained Bayesian Optimization with Application to Aircraft Design—0
BOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity search—0
Planning from Images with Deep Latent Gaussian Process DynamicsCode0
Regret Bounds for Safe Gaussian Process Bandit Optimization—0
Evaluation of Deep Gaussian Processes for Text Classification—0
Scaled Vecchia approximation for fast computer-model emulationCode0
On Bayesian Search for the Feasible Space Under Computationally Expensive ConstraintsCode0
Learning Constrained Dynamics with Gauss Principle adhering Gaussian ProcessesCode0
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Benchmark Results

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