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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 1626–1650 of 1963 papers

TitleStatusHype
Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regressionCode0
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distributionCode0
Gaussian Processes for Probabilistic Estimates of Earthquake Ground Shaking: A 1-D Proof-of-ConceptCode0
Shallow and Deep Nonparametric Convolutions for Gaussian ProcessesCode0
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO AlgorithmCode0
Shared Stochastic Gaussian Process Latent Variable Models: A Multi-modal Generative Model for Quasar SpectraCode0
Provable Quantum Algorithm Advantage for Gaussian Process QuadratureCode0
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia LanguageCode0
Efficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functionsCode0
Provably Reliable Large-Scale Sampling from Gaussian ProcessesCode0
What do you Mean? The Role of the Mean Function in Bayesian OptimisationCode0
Structured Variational Inference in Unstable Gaussian Process State Space ModelsCode0
Adaptive Cholesky Gaussian ProcessesCode0
Modelling stellar activity with Gaussian process regression networksCode0
Benefits of Monotonicity in Safe Exploration with Gaussian ProcessesCode0
Uncertainty quantification using martingales for misspecified Gaussian processesCode0
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational modelsCode0
Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple KernelCode0
Gaussian processes with linear operator inequality constraintsCode0
Quantile Propagation for Wasserstein-Approximate Gaussian ProcessesCode0
Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing EnsemblesCode0
Data-Driven Stochastic AC-OPF using Gaussian ProcessesCode0
Modular Jump Gaussian ProcessesCode0
Gaussian Process-Gated Hierarchical Mixtures of ExpertsCode0
Gaussian Process Kernels for Pattern Discovery and ExtrapolationCode0
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Benchmark Results

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