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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 951–960 of 1963 papers

TitleStatusHype
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation—0
One-parameter family of acquisition functions for efficient global optimization—0
Finite sample approximations of exact and entropic Wasserstein distances between covariance operators and Gaussian processes—0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional StructureCode1
Safe Chance Constrained Reinforcement Learning for Batch Process ControlCode0
Correlated Dynamics in Marketing Sensitivities—0
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
Mixtures of Gaussian Processes for regression under multiple prior distributions—0
Convolutional Normalizing Flows for Deep Gaussian Processes—0
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Benchmark Results

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