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 1021–1030 of 1963 papers

TitleStatusHype
Robust Super-Level Set Estimation using Gaussian Processes—0
Rough multifactor volatility for SPX and VIX options—0
Safe Active Learning for Time-Series Modeling with Gaussian Processes—0
Safe Bayesian Optimization for Complex Control Systems via Additive Gaussian Processes—0
Safety-Critical Learning of Robot Control with Temporal Logic Specifications—0
Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization—0
Safe Learning of Quadrotor Dynamics Using Barrier Certificates—0
Safe Machine-Learning-supported Model Predictive Force and Motion Control in Robotics—0
Safe model-based design of experiments using Gaussian processes—0
Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions—0
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Benchmark Results

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