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Implementation of random fourier feature (RFF) approximations and Thompson sampling.

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pyrff: Approximating Gaussian Process samples with Random Fourier Features

This project is a Python implementation of random fourier feature (RFF) approximations [1].

It is heavily inspired by the implementations from [2, 3] and generalizes the implementation to work with GP hyperparameters obtained from any GP library.

Examples are given as Jupyter notebooks for GPs fitted with PyMC3:

References

  1. Hernández-Lobato, 2014 paper, code
  2. PES implementation in Cornell-MOE code
  3. Bradford, 2018 paper, code

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