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 1541–1550 of 1963 papers

TitleStatusHype
Consistency of some sequential experimental design strategies for excursion set estimation based on vector-valued Gaussian processes—0
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck—0
Constrained Bayesian Optimization under Bivariate Gaussian Process with Application to Cure Process Optimization—0
Constraining Gaussian processes for physics-informed acoustic emission mapping—0
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations—0
Constructing Gaussian Processes via Samplets—0
Contextual Combinatorial Multi-output GP Bandits with Group Constraints—0
Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problems—0
Continuous-time edge modelling using non-parametric point processes—0
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces—0
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Benchmark Results

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