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 1031–1040 of 1963 papers

TitleStatusHype
Safe Online Learning-based Formation Control of Multi-Agent Systems with Gaussian Processes—0
Safe Optimization of an Industrial Refrigeration Process Using an Adaptive and Explorative Framework—0
Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes—0
Safe Stabilization with Model Uncertainties: A Universal Formula with Gaussian Process Learning—0
Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel—0
Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization—0
Safety-Aware Learning-Based Control of Systems with Uncertainty Dependent Constraints (extended version)—0
Safety Guarantees for Planning Based on Iterative Gaussian Processes—0
Safety Verification of Unknown Dynamical Systems via Gaussian Process Regression—0
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates—0
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Benchmark Results

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