Modular Gaussian Processes for Transfer Learning

🧩 Introduction

This repository contains the implementation of our paper Modular Gaussian Processes for Transfer Learning accepted in the 35th Conference on Neural Information Processing Systems (NeurIPS) 2021. The entire code is written in Python and is based on the Pytorch framework.

🧩 Idea

Here, you may find a new framework for transfer learning based on modular Gaussian processes (GP). The underlying idea is to avoid the revisiting of samples once a model is trained and well-fitted, so the model can be repurposed in combination with other or new data. We build dictionaries of modules (models), where each one contains only parameters and hyperparameters, but not observations. Finally, we are able to build meta-models (GP models) from different combinations of modules without reusing the old data.

🧩 Citation

Please, if you use this code, include the following citation:

@inproceedings{MorenoArtesAlvarez21,
  title =  {Modular {G}aussian Processes for Transfer Learning},
  author =   {Moreno-Mu\~noz, Pablo and Art\'es-Rodr\'iguez, Antonio and \'Alvarez, Mauricio A},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year =   {2021}
}

🧩 Usage

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🧩 Practical Examples

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