collabclass

Reproducibility package for the paper:

Lucas Maystre, Nagarjuna Kumarappan, Judith Bütepage, Mounia Lalmas. Collaborative Classification from Noisy Labels, AISTATS 2021.

This repository contains

a reference implementation of the algorithms presented in the paper, and
Jupyter notebooks enabling the reproduction of some of the experiments.

Getting started

Our codebase was tested with Python 3.8. The following libraries are required:

numpy (tested with version 1.19.2)
scipy (tested with version 1.6.2)
matplotlib (tested with version 3.3.4)
numba (tested with version 0.53.1)
notebook (tested with version 6.3.0)

To get started, follow these steps:

Clone the repo locally with: git clone https://github.com/spotify-research/collabclass.git
Move to the repository: cd collabclass
Install the dependencies: pip install -r requirements.txt
Install the package: pip install -e lib/
Move to the notebook folder: cd notebooks
Start a notebook server: jupyter notebok

Support

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Contributing

We feel that a welcoming community is important and we ask that you follow Spotify's Open Source Code of Conduct in all interactions with the community.

Author

Lucas Maystre

A full list of contributors can be found on GitHub.

Follow @SpotifyResearch on Twitter for updates.

License

Licensed under the Apache License, Version 2.0: https://www.apache.org/licenses/LICENSE-2.0

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