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 1601–1625 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
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Benchmark Results

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