Archives for Gaussian Process
Guide To GPBoost: A Library To Combine Tree-Boosting With Gaussian Process And Mixed-Effects Models
GPBoost is an approach and a software library aimed at combining tree-boosting with mixed-effects models and Gaussian Process (GP); hence the name ‘GP + Tree-Boosting’.
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GPyTorch is a PyTorch-based library designed for implementing Gaussian processes. It was introduced by Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger and Andrew Gordon Wilson – researchers at Cornel University (research paper). Before going into the details of GPyTorch, let us first understand what a Gaussian process means, in short. Gaussian Process…
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GPyTorch is a PyTorch-based library designed for implementing Gaussian processes. It was introduced by Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger and Andrew Gordon Wilson – researchers at Cornel University (research paper). Before going into the details of GPyTorch, let us first understand what a Gaussian process means, in short. Gaussian Process…
The post Guide To GPyTorch: A Python Library For Gaussian Process Models appeared first on Analytics India Magazine.
BoTorch is a library built on top of PyTorch for Bayesian Optimization. It combines Monte-Carlo (MC) acquisition functions, a novel sample average approximation optimization approach, auto-differentiation, and variance reduction techniques.
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