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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 1051–1075 of 1963 papers

TitleStatusHype
Variance-Reducing Couplings for Random Features—0
Variance Reduction for Matrix Computations with Applications to Gaussian Processes—0
Variational Auto-encoded Deep Gaussian Processes—0
Black-Box Inference for Non-Linear Latent Force Models—0
Variational Calibration of Computer Models—0
Variational Elliptical Processes—0
Variational Gaussian Processes: A Functional Analysis View—0
Variational Gaussian Processes For Linear Inverse Problems—0
Variational Gaussian Process State-Space Models—0
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models—0
Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models - a Gentle Tutorial—0
Variational Mixture of Gaussian Process Experts—0
Variational Nearest Neighbor Gaussian Process—0
VBALD - Variational Bayesian Approximation of Log Determinants—0
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks—0
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels—0
Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes—0
Bayesian Circular Regression with von Mises Quasi-Processes—0
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes—0
Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference—0
Wasserstein Barycenter Gaussian Process based Bayesian Optimization—0
Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference—0
Weakly-supervised Multi-output Regression via Correlated Gaussian Processes—0
What's Wrong With That Object? Identifying Images of Unusual Objects by Modelling the Detection Score Distribution—0
Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes—0
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Benchmark Results

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