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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 1526–1550 of 1963 papers

TitleStatusHype
Thoughts on Massively Scalable Gaussian ProcessesCode0
Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded VarianceCode0
Bayesian Deep Learning on a Quantum ComputerCode0
Scaled Vecchia approximation for fast computer-model emulationCode0
Log-Linear-Time Gaussian Processes Using Binary Tree KernelsCode0
The Debiased Spatial Whittle LikelihoodCode0
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian ProcessesCode0
Modeling longitudinal data using matrix completionCode0
Fast Approximate Multi-output Gaussian ProcessesCode0
Practical and Rigorous Uncertainty Bounds for Gaussian Process RegressionCode0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
Longitudinal prediction of DNA methylation to forecast epigenetic outcomesCode0
Practical multi-fidelity machine learning: fusion of deterministic and Bayesian modelsCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
Deep convolutional Gaussian processesCode0
Voronoi Candidates for Bayesian OptimizationCode0
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel DerivativesCode0
Scale Mixtures of Neural Network Gaussian ProcessesCode0
Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep ClassificationCode0
Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processesCode0
Scaling Gaussian Process Regression with DerivativesCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
On out-of-distribution detection with Bayesian neural networksCode0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Accounting for Gaussian Process Imprecision in Bayesian OptimizationCode0
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Benchmark Results

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