---
OA_place: repository
OA_type: green
_id: '18971'
abstract:
- lang: eng
  text: 'Models prone to spurious correlations in training data often produce brittle
    predictions and introduce unintended biases. Addressing this challenge typically
    involves methods relying on prior knowledge and group annotation to remove spurious
    correlations, which may not be readily available in many applications. In this
    paper, we establish a novel connection between unsupervised object-centric learning
    and mitigation of spurious correlations. Instead of directly inferring subgroups
    with varying correlations with labels, our approach focuses on discovering concepts:
    discrete ideas that are shared across input samples. Leveraging existing object-centric
    representation learning, we introduce CoBalT: a concept balancing technique that
    effectively mitigates spurious correlations without requiring human labeling of
    subgroups. Evaluation across the benchmark datasets for sub-population shifts
    demonstrate superior or competitive performance compared state-of-the-art baselines,
    without the need for group annotation. Code is available at https://github.com/rarefin/CoBalT'
acknowledgement: "We acknowledge the support of the Canada CIFAR AI Chair Program
  and IVADO. We thank Mila and Compute Canada for providing computational resources.\r\n"
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Rifat
  full_name: Arefin, Rifat
  last_name: Arefin
- first_name: Yan
  full_name: Zhang, Yan
  last_name: Zhang
- first_name: Aristide
  full_name: Baratin, Aristide
  last_name: Baratin
- first_name: Francesco
  full_name: Locatello, Francesco
  id: 26cfd52f-2483-11ee-8040-88983bcc06d4
  last_name: Locatello
  orcid: 0000-0002-4850-0683
- first_name: Irina
  full_name: Rish, Irina
  last_name: Rish
- first_name: Dianbo
  full_name: Liu, Dianbo
  last_name: Liu
- first_name: Kenji
  full_name: Kawaguchi, Kenji
  last_name: Kawaguchi
citation:
  ama: 'Arefin R, Zhang Y, Baratin A, et al. Unsupervised concept discovery mitigates
    spurious correlations. In: <i>Proceedings of the 41st International Conference
    on Machine Learning</i>. Vol 235. ML Research Press; 2024:1672-1688.'
  apa: 'Arefin, R., Zhang, Y., Baratin, A., Locatello, F., Rish, I., Liu, D., &#38;
    Kawaguchi, K. (2024). Unsupervised concept discovery mitigates spurious correlations.
    In <i>Proceedings of the 41st International Conference on Machine Learning</i>
    (Vol. 235, pp. 1672–1688). Vienna, Austria: ML Research Press.'
  chicago: Arefin, Rifat, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina
    Rish, Dianbo Liu, and Kenji Kawaguchi. “Unsupervised Concept Discovery Mitigates
    Spurious Correlations.” In <i>Proceedings of the 41st International Conference
    on Machine Learning</i>, 235:1672–88. ML Research Press, 2024.
  ieee: R. Arefin <i>et al.</i>, “Unsupervised concept discovery mitigates spurious
    correlations,” in <i>Proceedings of the 41st International Conference on Machine
    Learning</i>, Vienna, Austria, 2024, vol. 235, pp. 1672–1688.
  ista: 'Arefin R, Zhang Y, Baratin A, Locatello F, Rish I, Liu D, Kawaguchi K. 2024.
    Unsupervised concept discovery mitigates spurious correlations. Proceedings of
    the 41st International Conference on Machine Learning. ICML: International Conference
    on Machine Learning, PMLR, vol. 235, 1672–1688.'
  mla: Arefin, Rifat, et al. “Unsupervised Concept Discovery Mitigates Spurious Correlations.”
    <i>Proceedings of the 41st International Conference on Machine Learning</i>, vol.
    235, ML Research Press, 2024, pp. 1672–88.
  short: R. Arefin, Y. Zhang, A. Baratin, F. Locatello, I. Rish, D. Liu, K. Kawaguchi,
    in:, Proceedings of the 41st International Conference on Machine Learning, ML
    Research Press, 2024, pp. 1672–1688.
conference:
  end_date: 2024-07-27
  location: Vienna, Austria
  name: 'ICML: International Conference on Machine Learning'
  start_date: 2024-07-21
date_created: 2025-01-30T07:21:57Z
date_published: 2024-07-30T00:00:00Z
date_updated: 2025-01-30T07:23:10Z
day: '30'
department:
- _id: FrLo
external_id:
  arxiv:
  - '2402.13368'
intvolume: '       235'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.48550/arXiv.2402.13368
month: '07'
oa: 1
oa_version: Preprint
page: 1672-1688
publication: Proceedings of the 41st International Conference on Machine Learning
publication_identifier:
  eissn:
  - 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
  link:
  - relation: software
    url: https://github.com/rarefin/CoBalT
scopus_import: '1'
status: public
title: Unsupervised concept discovery mitigates spurious correlations
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 235
year: '2024'
...