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 391–400 of 1963 papers

TitleStatusHype
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian ProcessesCode0
Genus expansion for non-linear random matrix ensembles with applications to neural networks—0
Implementation and Analysis of GPU Algorithms for Vecchia ApproximationCode0
Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference—0
Adaptive RKHS Fourier Features for Compositional Gaussian Process ModelsCode0
DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety—0
Diffusion-BBO: Diffusion-Based Inverse Modeling for Online Black-Box Optimization—0
Learning Time-Varying Multi-Region Communications via Scalable Markovian Gaussian Processes—0
Permutation invariant multi-output Gaussian Processes for drug combination prediction in cancer—0
Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes—0
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Benchmark Results

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