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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 1476–1500 of 1963 papers

TitleStatusHype
Bayesian Optimization by Kernel Regression and Density-based Exploration—0
Bayesian optimization explains human active search—0
Bayesian Optimization of Bilevel Problems—0
Bayesian optimization of distributed neurodynamical controller models for spatial navigation—0
Bayesian Optimization via Continual Variational Last Layer Training—0
Bayesian Optimization with Informative Covariance—0
Bayesian Optimization with Tree-structured Dependencies—0
Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers—0
Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables—0
Bayesian Quantile and Expectile Optimisation—0
Bayesian Relational Generative Model for Scalable Multi-modal Learning—0
Bayesian Sparse Factor Analysis with Kernelized Observations—0
Bayesian Variational Optimization for Combinatorial Spaces—0
Bayesian Warped Gaussian Processes—0
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction—0
BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems—0
Beyond IID weights: sparse and low-rank deep Neural Networks are also Gaussian Processes—0
Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei—0
Bézier Curve Gaussian Processes—0
Bézier Gaussian Processes for Tall and Wide Data—0
BI-EqNO: Generalized Approximate Bayesian Inference with an Equivariant Neural Operator Framework—0
Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields—0
Blitzkriging: Kronecker-structured Stochastic Gaussian Processes—0
BOIS: Bayesian Optimization of Interconnected Systems—0
BOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity search—0
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Benchmark Results

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