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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 1031–1040 of 1963 papers

TitleStatusHype
Fractional Barndorff-Nielsen and Shephard model: applications in variance and volatility swaps, and hedging—0
Numerical Gaussian process Kalman filtering for spatiotemporal systems—0
How Bayesian Should Bayesian Optimisation Be?Code0
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation—0
Finite sample approximations of exact and entropic Wasserstein distances between covariance operators and Gaussian processes—0
One-parameter family of acquisition functions for efficient global optimization—0
Correlated Dynamics in Marketing Sensitivities—0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
Safe Chance Constrained Reinforcement Learning for Batch Process ControlCode0
Mixtures of Gaussian Processes for regression under multiple prior distributions—0
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Benchmark Results

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