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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 201–210 of 1963 papers

TitleStatusHype
Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes—0
Practical multi-fidelity machine learning: fusion of deterministic and Bayesian modelsCode0
Data-Driven Abstractions via Binary-Tree Gaussian Processes for Formal Verification—0
Inference at the data's edge: Gaussian processes for modeling and inference under model-dependency, poor overlap, and extrapolation—0
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
The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and GraphsCode4
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
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Benchmark Results

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