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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 726–750 of 1963 papers

TitleStatusHype
Provably Reliable Large-Scale Sampling from Gaussian ProcessesCode0
Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes—0
Learning Neural Optimal Interpolation Models and Solvers—0
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice—0
Towards Improved Learning in Gaussian Processes: The Best of Two Worlds—0
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO AlgorithmCode0
On power sum kernels on symmetric groups—0
Fully Bayesian inference for latent variable Gaussian process modelsCode0
Black-box Coreset Variational InferenceCode0
Multi-output Gaussian processes for inverse uncertainty quantification in neutron noise analysis—0
Isotropic Gaussian Processes on Finite Spaces of GraphsCode0
Benefits of Monotonicity in Safe Exploration with Gaussian ProcessesCode0
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning—0
Monte Carlo Tree Descent for Black-Box Optimization—0
Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models—0
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal TransportCode0
Optimization on Manifolds via Graph Gaussian Processes—0
Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning—0
Scalable Bayesian Transformed Gaussian Processes—0
Locally Smoothed Gaussian Process Regression—0
Conditional Neural Processes for Molecules—0
Model of rough surfaces with Gaussian processes—0
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover TreesCode0
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes—0
Gaussian Processes on Distributions based on Regularized Optimal Transport—0
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Benchmark Results

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