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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 1101–1125 of 1963 papers

TitleStatusHype
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models—0
Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature—0
Exponentially Stable Projector-based Control of Lagrangian Systems with Gaussian Processes—0
Extended and Unscented Gaussian Processes—0
Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice—0
Extracting Predictive Information from Heterogeneous Data Streams using Gaussian Processes—0
Extrinsic Bayesian Optimizations on Manifolds—0
Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes—0
Facility Deployment Decisions through Warp Optimizaton of Regressed Gaussian Processes—0
Fairness-aware Bayes optimal functional classification—0
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning—0
Fast Adaptation with Linearized Neural Networks—0
Fast Adaptive Weight Noise—0
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning—0
Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations—0
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression—0
Fast Design Space Exploration of Nonlinear Systems: Part I—0
Fast emulation of density functional theory simulations using approximate Gaussian processes—0
Faster Kernel Interpolation for Gaussian Processes—0
Faster variational inducing input Gaussian process classification—0
Fast Gaussian Processes under Monotonicity Constraints—0
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation—0
Fast Gaussian Process Regression for Big Data—0
Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes—0
Fast Kernel Learning for Multidimensional Pattern Extrapolation—0
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Benchmark Results

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