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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 1–25 of 1963 papers

TitleStatusHype
Chronos: Learning the Language of Time SeriesCode7
The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and GraphsCode4
Adversarial Robustness Toolbox v1.0.0Code3
A Framework for Interdomain and Multioutput Gaussian ProcessesCode2
MathOptAI.jl: Embed trained machine learning predictors into JuMP modelsCode2
High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraftCode2
Statistical Machine Learning for Astronomy -- A TextbookCode2
GAUCHE: A Library for Gaussian Processes in ChemistryCode2
L4acados: Learning-based models for acados, applied to Gaussian process-based predictive controlCode2
SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian ProcessesCode2
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlowCode2
Real-time Spatial-temporal Traversability Assessment via Feature-based Sparse Gaussian ProcessCode2
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear AlgebraCode2
GPflow: A Gaussian process library using TensorFlowCode2
Convolutional Gaussian ProcessesCode2
Gaussian Processes for Big DataCode2
Bayesian Deep Learning and a Probabilistic Perspective of GeneralizationCode1
70 years of machine learning in geoscience in reviewCode1
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian ProcessesCode1
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
Pre-trained Gaussian Processes for Bayesian OptimizationCode1
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
Bayesian Deep Ensembles via the Neural Tangent KernelCode1
Batched Energy-Entropy acquisition for Bayesian OptimizationCode1
Variational multiple shooting for Bayesian ODEs with Gaussian processesCode1
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Benchmark Results

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