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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

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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

Copyright 2021 Spotify, Inc.

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

Security Issues?

Please report sensitive security issues via Spotify's bug-bounty program (https://hackerone.com/spotify) rather than GitHub.

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Code for the paper "Collaborative Classification from Noisy Labels".

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