{"status":"public","day":"30","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2402.13368"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","OA_type":"green","publication_status":"published","external_id":{"arxiv":["2402.13368"]},"conference":{"name":"ICML: International Conference on Machine Learning","start_date":"2024-07-21","end_date":"2024-07-27","location":"Vienna, Austria"},"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","abstract":[{"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","lang":"eng"}],"publisher":"ML Research Press","publication":"Proceedings of the 41st International Conference on Machine Learning","related_material":{"link":[{"relation":"software","url":"https://github.com/rarefin/CoBalT"}]},"arxiv":1,"citation":{"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.","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.","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.","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.","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.","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.","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."},"volume":235,"title":"Unsupervised concept discovery mitigates spurious correlations","date_updated":"2025-01-30T07:23:10Z","quality_controlled":"1","year":"2024","month":"07","scopus_import":"1","publication_identifier":{"eissn":["2640-3498"]},"article_processing_charge":"No","language":[{"iso":"eng"}],"oa":1,"department":[{"_id":"FrLo"}],"page":"1672-1688","date_created":"2025-01-30T07:21:57Z","alternative_title":["PMLR"],"OA_place":"repository","author":[{"last_name":"Arefin","first_name":"Rifat","full_name":"Arefin, Rifat"},{"full_name":"Zhang, Yan","first_name":"Yan","last_name":"Zhang"},{"first_name":"Aristide","last_name":"Baratin","full_name":"Baratin, Aristide"},{"last_name":"Locatello","first_name":"Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco"},{"full_name":"Rish, Irina","first_name":"Irina","last_name":"Rish"},{"first_name":"Dianbo","last_name":"Liu","full_name":"Liu, Dianbo"},{"full_name":"Kawaguchi, Kenji","last_name":"Kawaguchi","first_name":"Kenji"}],"oa_version":"Preprint","_id":"18971","type":"conference","intvolume":"       235","date_published":"2024-07-30T00:00:00Z"}