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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 1601–1650 of 1963 papers

TitleStatusHype
Bias-Free Scalable Gaussian Processes via Randomized TruncationsCode0
Variational Learning on Aggregate Outputs with Gaussian ProcessesCode0
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive UncertaintiesCode0
Mixture-based Multiple Imputation Model for Clinical Data with a Temporal DimensionCode0
Structured and Efficient Variational Deep Learning with Matrix Gaussian PosteriorsCode0
Mixtures of Gaussian process experts based on kernel stick-breaking processesCode0
Gaussian Process Behaviour in Wide Deep Neural NetworksCode0
Probabilistic Robust Linear Quadratic Regulators with Gaussian ProcessesCode0
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variabilityCode0
Mode-constrained Model-based Reinforcement Learning via Gaussian ProcessesCode0
Beyond Intuition, a Framework for Applying GPs to Real-World DataCode0
Sequential Gaussian Processes for Online Learning of Nonstationary FunctionsCode0
Probabilistic Subgoal Representations for Hierarchical Reinforcement learningCode0
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive ControlCode0
Model Criticism in Latent SpaceCode0
Efficient Inference in Multi-task Cox Process ModelsCode0
Modèles de Substitution pour les Modèles à base d'Agents : Enjeux, Méthodes et ApplicationsCode0
Sequential Learning of Active SubspacesCode0
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming LanguageCode0
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF SurrogatesCode0
Gaussian Processes for Data-Efficient Learning in Robotics and ControlCode0
Sequential Neural ProcessesCode0
Model-based Reinforcement Learning for Continuous Control with Posterior SamplingCode0
Gaussian Processes for Monitoring Air-Quality in KampalaCode0
Promises and Pitfalls of the Linearized Laplace in Bayesian OptimizationCode0
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