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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 401–425 of 1963 papers

Title	Date	Tasks	Status	Score
Kernel-, mean- and noise-marginalised Gaussian processes for exoplanet transits and H_0 inference	Nov 7, 2023	Gaussian Processes	CodeCode Available	5
Adversarial Robustness Guarantees for Random Deep Neural Networks	Apr 13, 2020	Adversarial RobustnessGaussian Processes	CodeCode Available	5
Data-driven Aerodynamic Analysis of Structures using Gaussian Processes	Mar 20, 2021	Gaussian Processes	CodeCode Available	5
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation	May 23, 2016	Gaussian Processes	CodeCode Available	5
Estimation of Dynamic Gaussian Processes	Nov 29, 2023	Gaussian Processes	CodeCode Available	5
Evaluating the squared-exponential covariance function in Gaussian processes with integral observations	Dec 18, 2018	Gaussian Processes	CodeCode Available	5
Entropic Trace Estimates for Log Determinants	Apr 24, 2017	Gaussian ProcessesPoint Processes	CodeCode Available	5
Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes	Aug 30, 2022	Gaussian Processes	CodeCode Available	5
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks	Mar 21, 2019	continuous-controlContinuous Control	CodeCode Available	5
Last Layer Marginal Likelihood for Invariance Learning	Jun 14, 2021	Data AugmentationGaussian Processes	CodeCode Available	5
Latent Variable Multi-output Gaussian Processes for Hierarchical Datasets	Aug 31, 2023	Gaussian Processes	CodeCode Available	5
Learning Choice Functions with Gaussian Processes	Feb 1, 2023	Gaussian Processes	CodeCode Available	5
Learning Constrained Dynamics with Gauss' Principle adhering Gaussian Processes	Jun 8, 2020	Gaussian Processesregression	CodeCode Available	5
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks	Sep 12, 2018	Gaussian ProcessesMixture-of-Experts	CodeCode Available	5
Epistemic Uncertainty in Conformal Scores: A Unified Approach	Feb 10, 2025	Conformal PredictionGaussian Processes	CodeCode Available	5
Data-Driven Stochastic AC-OPF using Gaussian Processes	Feb 17, 2024	Gaussian ProcessesGPR	CodeCode Available	5
Adversarial Robustness Guarantees for Gaussian Processes	Apr 7, 2021	Adversarial RobustnessGaussian Processes	CodeCode Available	5
Autoencoder Attractors for Uncertainty Estimation	Apr 1, 2022	Gaussian Processes	CodeCode Available	5
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes	Nov 25, 2020	Gaussian ProcessesTransfer Learning	CodeCode Available	5
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control	Jun 20, 2017	Gaussian ProcessesModel Predictive Control	CodeCode Available	5
A Learnable Safety Measure	Oct 7, 2019	Gaussian Processes	CodeCode Available	5
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes	May 9, 2018	Bayesian OptimizationGaussian Processes	CodeCode Available	5
The Debiased Spatial Whittle Likelihood	Jul 4, 2019	Gaussian Processes	CodeCode Available	5
Automatic Construction and Natural-Language Description of Nonparametric Regression Models	Feb 18, 2014	Gaussian Processesregression	CodeCode Available	5
Approximate Latent Force Model Inference	Sep 24, 2021	Gaussian Processesmodel	CodeCode Available	5

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ICKy, periodic	Root mean square error (RMSE)	0.03	—	Unverified