{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T02:31:20Z","timestamp":1776133880169,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"LabEx CIMI","award":["ANR-11-LABX-0040"],"award-info":[{"award-number":["ANR-11-LABX-0040"]}]},{"DOI":"10.13039\/501100001804","name":"Canada Research Chairs","doi-asserted-by":"publisher","award":["Privacy-preserving and ethical analysis of Big Data"],"award-info":[{"award-number":["Privacy-preserving and ethical analysis of Big Data"]}],"id":[{"id":"10.13039\/501100001804","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s10994-022-06191-y","type":"journal-article","created":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T19:37:47Z","timestamp":1657136267000},"page":"2131-2192","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Improving fairness generalization through a sample-robust optimization method"],"prefix":"10.1007","volume":"112","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8764-0080","authenticated-orcid":false,"given":"Julien","family":"Ferry","sequence":"first","affiliation":[]},{"given":"Ulrich","family":"A\u00efvodji","sequence":"additional","affiliation":[]},{"given":"S\u00e9bastien","family":"Gambs","sequence":"additional","affiliation":[]},{"given":"Marie-Jos\u00e9","family":"Huguet","sequence":"additional","affiliation":[]},{"given":"Mohamed","family":"Siala","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,6]]},"reference":[{"key":"6191_CR1","unstructured":"Agarwal, A., Beygelzimer, A., Dudik, M., et\u00a0al. (2018). A reductions approach to fair classification. In Proceedings of the 35th International Conference on Machine Learning, Proceedings of Machine Learning Research (Vol.\u00a080, pp 60\u201369). PMLR. https:\/\/proceedings.mlr.press\/v80\/agarwal18a.html"},{"key":"6191_CR2","unstructured":"A\u00efvodji, U., Ferry, J., Gambs, S., et\u00a0al. (2019). Learning fair rule lists. arXiv preprint arXiv:1909.03977."},{"key":"6191_CR3","doi-asserted-by":"publisher","unstructured":"A\u00efvodji, U., Ferry, J., Gambs, S., et\u00a0al. (2021). Faircorels, an open-source library for learning fair rule lists. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. Association for Computing Machinery, New York, NY, USA, CIKM \u201921 (pp. 4665\u20134669). https:\/\/doi.org\/10.1145\/3459637.3481965.","DOI":"10.1145\/3459637.3481965"},{"key":"6191_CR4","doi-asserted-by":"publisher","unstructured":"Angelino, E., Larus-Stone, N., Alabi, D., et\u00a0al. (2017). Learning certifiably optimal rule lists. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, KDD \u201917 (pp. 35\u201344). https:\/\/doi.org\/10.1145\/3097983.3098047.","DOI":"10.1145\/3097983.3098047"},{"issue":"234","key":"6191_CR5","first-page":"1","volume":"18","author":"E Angelino","year":"2018","unstructured":"Angelino, E., Larus-Stone, N., Alabi, D., et al. (2018). Learning certifiably optimal rule lists for categorical data. Journal of Machine Learning Research, 18(234), 1\u201378.","journal-title":"Journal of Machine Learning Research"},{"key":"6191_CR6","unstructured":"Angwin, J., Larson, J., Mattu, S., et\u00a0al. (2016). Machine bias: There\u2019s software used across the country to predict future criminals. and it\u2019s biased against blacks. propublica (2016). ProPublica, May 23."},{"key":"6191_CR7","unstructured":"Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning. fairmlbook.org, http:\/\/www.fairmlbook.org."},{"issue":"2","key":"6191_CR8","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1287\/mnsc.1120.1641","volume":"59","author":"A Ben-Tal","year":"2013","unstructured":"Ben-Tal, A., Den Hertog, D., De Waegenaere, A., et al. (2013). Robust solutions of optimization problems affected by uncertain probabilities. Management Science, 59(2), 341\u2013357. https:\/\/doi.org\/10.1287\/mnsc.1120.1641.","journal-title":"Management Science"},{"key":"6191_CR9","unstructured":"Caton, S., & Haas, C. (2020). Fairness in machine learning: A survey. arXiv preprint arXiv:2010.04053."},{"issue":"2","key":"6191_CR10","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1089\/big.2016.0047","volume":"5","author":"A Chouldechova","year":"2017","unstructured":"Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data, 5(2), 153\u2013163. https:\/\/doi.org\/10.1089\/big.2016.0047.","journal-title":"Big Data"},{"key":"6191_CR11","unstructured":"Chuang, C. Y., & Mroueh, Y. (2021). Fair mixup: Fairness via interpolation. In: 9th International Conference on Learning Representations, ICLR, https:\/\/openreview.net\/forum?id=DNl5s5BXeBn."},{"key":"6191_CR12","unstructured":"Cotter, A., Gupta, M., Jiang, H., et\u00a0al. (2018). Training fairness-constrained classifiers to generalize. FATML."},{"key":"6191_CR13","unstructured":"Cotter, A., Gupta, M., Jiang, H., et\u00a0al. (2019a). Training well-generalizing classifiers for fairness metrics and other data-dependent constraints. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, Proceedings of Machine Learning Research (Vol.\u00a097, pp. 1397\u20131405). PMLR. http:\/\/proceedings.mlr.press\/v97\/cotter19b.html."},{"key":"6191_CR14","unstructured":"Cotter, A., Jiang, H., & Sridharan, K. (2019b). Two-player games for efficient non-convex constrained optimization. In Algorithmic Learning Theory, ALT 2019, 22-24 March 2019, Chicago, Illinois, USA, Proceedings of Machine Learning Research (Vol.\u00a098, pp. 300\u2013332). PMLR. http:\/\/proceedings.mlr.press\/v98\/cotter19a.html."},{"key":"6191_CR15","doi-asserted-by":"publisher","unstructured":"Cummings, R., Gupta, V., Kimpara, D., et\u00a0al. (2019). On the compatibility of privacy and fairness. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, New York, NY, USA, UMAP\u201919 Adjunct (pp. 309\u2013315). https:\/\/doi.org\/10.1145\/3314183.3323847","DOI":"10.1145\/3314183.3323847"},{"key":"6191_CR16","doi-asserted-by":"publisher","unstructured":"Du, W., & Wu, X. (2021). Fair and robust classification under sample selection bias. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. Association for Computing Machinery, New York, NY, USA, CIKM \u201921 (pp. 2999\u20133003). https:\/\/doi.org\/10.1145\/3459637.3482104.","DOI":"10.1145\/3459637.3482104"},{"key":"6191_CR17","unstructured":"Duchi, J. C., Hashimoto, T., & Namkoong, H. (2020). Distributionally robust losses for latent covariate mixtures. arXiv preprint arXiv:2007.13982."},{"issue":"3","key":"6191_CR18","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1287\/moor.2020.1085","volume":"46","author":"JC Duchi","year":"2021","unstructured":"Duchi, J. C., Glynn, P. W., & Namkoong, H. (2021). Statistics of robust optimization: A generalized empirical likelihood approach. Mathematics of Operations Research, 46(3), 946\u2013969. https:\/\/doi.org\/10.1287\/moor.2020.1085.","journal-title":"Mathematics of Operations Research"},{"issue":"3\u20134","key":"6191_CR19","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1561\/0400000042","volume":"9","author":"C Dwork","year":"2014","unstructured":"Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Found Trends Theory Computer Science, 9(3\u20134), 211\u2013407. https:\/\/doi.org\/10.1561\/0400000042.","journal-title":"Found Trends Theory Computer Science"},{"key":"6191_CR20","doi-asserted-by":"publisher","unstructured":"Dwork, C., Hardt, M., Pitassi, T., et\u00a0al. (2012). Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference. Association for Computing Machinery, New York, NY, USA, ITCS \u201912 (pp. 214\u2013226). https:\/\/doi.org\/10.1145\/2090236.2090255","DOI":"10.1145\/2090236.2090255"},{"key":"6191_CR21","unstructured":"Frank, A., & Asuncion, A. (2010). UCI machine learning repository [http:\/\/archive.ics.uci.edu\/ml]. Irvine, CA: University of California. School of information and computer science 213:2\u20132"},{"issue":"1","key":"6191_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2594473.2594475","volume":"15","author":"AA Freitas","year":"2014","unstructured":"Freitas, A. A. (2014). Comprehensible classification models: A position paper. SIGKDD Explorations Newsletter, 15(1), 1\u201310. https:\/\/doi.org\/10.1145\/2594473.2594475.","journal-title":"SIGKDD Explorations Newsletter"},{"key":"6191_CR23","unstructured":"Hardt, M., Price, E., Price, E., et\u00a0al. (2016). Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems (Vol.\u00a029). Curran Associates, Inc., https:\/\/proceedings.neurips.cc\/paper\/2016\/file\/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf."},{"key":"6191_CR24","unstructured":"Huang, L., & Vishnoi, N. K. (2019). Stable and fair classification. In K. Chaudhuri, R. Salakhutdinov (Eds) Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, Proceedings of Machine Learning Research (Vol.\u00a097, pp. 2879\u20132890). PMLR. http:\/\/proceedings.mlr.press\/v97\/huang19e.html."},{"key":"6191_CR25","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1007\/978-3-030-58475-7_49","volume-title":"Principles and Practice of Constraint Programming","author":"Alexey Ignatiev","year":"2020","unstructured":"Ignatiev, A., Cooper, M. C., Siala, M., Hebrard, E., & Marques-Silva, J.  (2020). Towards formal fairness in machine learning. In Simonis, H. (Eds.),\u00a0Principles and practice of constraint programming. CP 2020. Lecture Notes in Computer Science (Vol 12333). Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-58475-7_49."},{"key":"6191_CR26","unstructured":"Iofinova, E., Konstantinov, N., & Lampert, C. H. (2021). Flea: Provably fair multisource learning from unreliable training data. arXiv preprint arXiv:2106.11732."},{"issue":"1","key":"6191_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-011-0463-8","volume":"33","author":"F Kamiran","year":"2012","unstructured":"Kamiran, F., & Calders, T. (2012). Data preprocessing techniques for classification without discrimination. Knowledge and Information Systems, 33(1), 1\u201333. https:\/\/doi.org\/10.1007\/s10115-011-0463-8.","journal-title":"Knowledge and Information Systems"},{"key":"6191_CR28","unstructured":"Kang, Y. (2017). Distributionally robust optimization and its applications in machine learning. PhD thesis, Columbia University."},{"key":"6191_CR29","doi-asserted-by":"publisher","unstructured":"Khoshgoftaar, T. M., Fazelpour, A., Wang, H., et\u00a0al. (2013). A survey of stability analysis of feature subset selection techniques. In 2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) (pp. 424\u2013431). https:\/\/doi.org\/10.1109\/IRI.2013.6642502","DOI":"10.1109\/IRI.2013.6642502"},{"key":"6191_CR30","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.patrec.2018.12.007","volume":"120","author":"S Kosub","year":"2019","unstructured":"Kosub, S. (2019). A note on the triangle inequality for the jaccard distance. Pattern Recognition Letters, 120, 36\u201338. https:\/\/doi.org\/10.1016\/j.patrec.2018.12.007.","journal-title":"Pattern Recognition Letters"},{"key":"6191_CR31","unstructured":"Liu, E. Z., Haghgoo, B., Chen, A. S., et\u00a0al. (2021). Just train twice: Improving group robustness without training group information. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, Proceedings of Machine Learning Research (Vol. 139, pp. 6781\u20136792). PMLR. http:\/\/proceedings.mlr.press\/v139\/liu21f.html."},{"key":"6191_CR32","unstructured":"Mandal, D., Deng, S., Jana, S., et\u00a0al. (2020). Ensuring fairness beyond the training data. In Advances in Neural Information Processing Systems (Vol.\u00a033, pp. 18,445\u201318,456). Curran Associates, Inc., https:\/\/proceedings.neurips.cc\/\/paper\/2020\/file\/d6539d3b57159babf6a72e106beb45bd-Paper.pdf."},{"key":"6191_CR33","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.dss.2014.03.001","volume":"62","author":"S Moro","year":"2014","unstructured":"Moro, S., Cortez, P., & Rita, P. (2014). A data-driven approach to predict the success of bank telemarketing. Decision Support Systems, 62, 22\u201331. https:\/\/doi.org\/10.1016\/j.dss.2014.03.001.","journal-title":"Decision Support Systems"},{"key":"6191_CR34","unstructured":"Nam, J., Cha, H., Ahn, S., et\u00a0al. (2020). Learning from failure: De-biasing classifier from biased classifier. In Advances in Neural Information Processing Systems (Vol.\u00a033, pp 20,673\u201320,684). Curran Associates, Inc., https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/eddc3427c5d77843c2253f1e799fe933-Paper.pdf"},{"key":"6191_CR35","unstructured":"Perron, L., & Furnon, V. (2019). Or-tools. https:\/\/developers.google.com\/optimization\/."},{"key":"6191_CR36","unstructured":"Rahimian, H., & Mehrotra, S. (2019). Distributionally robust optimization: A review. arXiv preprint arXiv:1908.05659."},{"key":"6191_CR37","doi-asserted-by":"publisher","unstructured":"Rezaei, A., Fathony, R., Memarrast, O., et\u00a0al. (2020). Fairness for robust log loss classification 5511\u20135518. https:\/\/doi.org\/10.1609\/aaai.v34i04.6002","DOI":"10.1609\/aaai.v34i04.6002"},{"issue":"3","key":"6191_CR38","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/BF00058680","volume":"2","author":"RL Rivest","year":"1987","unstructured":"Rivest, R. L. (1987). Learning decision lists. Machine Learning, 2(3), 229\u2013246. https:\/\/doi.org\/10.1007\/BF00058680.","journal-title":"Machine Learning"},{"issue":"5","key":"6191_CR39","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206\u2013215. https:\/\/doi.org\/10.1038\/s42256-019-0048-x.","journal-title":"Nature Machine Intelligence"},{"key":"6191_CR40","doi-asserted-by":"publisher","unstructured":"Saeys, Y., Abeel, T., & Van\u00a0de Peer, Y. (2008). Robust feature selection using ensemble feature selection techniques. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 313\u2013325). Springer. https:\/\/doi.org\/10.1007\/978-3-540-87481-2_21","DOI":"10.1007\/978-3-540-87481-2_21"},{"key":"6191_CR41","unstructured":"Sagawa, S., Koh, P. W., Hashimoto, T. B., et\u00a0al. (2020). Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, https:\/\/openreview.net\/forum?id=ryxGuJrFvS."},{"key":"6191_CR42","doi-asserted-by":"publisher","unstructured":"Slack, D., Friedler, S. A., & Givental, E. (2020). Fairness warnings and fair-maml: learning fairly with minimal data. In M. Hildebrandt, C. Castillo, L. E. Celis, et\u00a0al. (Eds) FAT* \u201920: Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, January 27-30, 2020 (pp. 200\u2013209). ACM. https:\/\/doi.org\/10.1145\/3351095.3372839","DOI":"10.1145\/3351095.3372839"},{"key":"6191_CR43","unstructured":"Taskesen, B., Nguyen, V. A., Kuhn, D., et\u00a0al. (2020). A distributionally robust approach to fair classification. arXiv preprint arXiv:2007.09530."},{"key":"6191_CR44","doi-asserted-by":"publisher","unstructured":"Tommasi, T., Patricia, N., Caputo, B., et\u00a0al. (2017). A deeper look at dataset bias. In Domain Adaptation in Computer Vision Applications. Advances in Computer Vision and Pattern Recognition (pp. 37\u201355). Springer. https:\/\/doi.org\/10.1007\/978-3-319-58347-1_2","DOI":"10.1007\/978-3-319-58347-1_2"},{"key":"6191_CR45","doi-asserted-by":"publisher","unstructured":"Torralba, A., & Efros, A. A. (2011). Unbiased look at dataset bias. In 2013 IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, Los Alamitos, CA, USA (pp. 1521\u20131528). https:\/\/doi.org\/10.1109\/CVPR.2011.5995347.","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"6191_CR46","doi-asserted-by":"publisher","unstructured":"Verma, S., & Rubin, J. (2018). Fairness definitions explained. In Proceedings of the International Workshop on Software Fairness. Association for Computing Machinery, New York, NY, USA, FairWare \u201918 (pp. 1\u20137). https:\/\/doi.org\/10.1145\/3194770.3194776","DOI":"10.1145\/3194770.3194776"},{"key":"6191_CR47","unstructured":"Wang, Y., Nguyen, V. A., & Hanasusanto, G. A. (2021). Wasserstein robust support vector machines with fairness constraints. arXiv preprint arXiv:2103.06828."},{"issue":"2, Part 1","key":"6191_CR48","doi-asserted-by":"publisher","first-page":"2473","DOI":"10.1016\/j.eswa.2007.12.020","volume":"36","author":"IC Yeh","year":"2009","unstructured":"Yeh, I. C., & hui Lien, C. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2, Part 1), 2473\u20132480. https:\/\/doi.org\/10.1016\/j.eswa.2007.12.020.","journal-title":"Expert Systems with Applications"},{"key":"6191_CR49","unstructured":"Yurochkin, M., Bower, A., & Sun, Y. (2020). Training individually fair ml models with sensitive subspace robustness. In 8th International Conference on Learning Representations, ICLR Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, https:\/\/openreview.net\/forum?id=B1gdkxHFDH."},{"key":"6191_CR50","doi-asserted-by":"publisher","unstructured":"Zafar, M. B., Valera, I., Gomez\u00a0Rodriguez, M., et\u00a0al. (2017). Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, WWW \u201917 (pp. 1171\u20131180). https:\/\/doi.org\/10.1145\/3038912.3052660","DOI":"10.1145\/3038912.3052660"},{"key":"6191_CR51","doi-asserted-by":"publisher","DOI":"10.1201\/b12207","volume-title":"Ensemble Methods: Foundations and Algorithms","author":"ZH Zhou","year":"2012","unstructured":"Zhou, Z. H. (2012). Ensemble Methods: Foundations and Algorithms (1st ed.). London: Chapman & Hall\/CRC. https:\/\/doi.org\/10.1201\/b12207.","edition":"1"},{"key":"6191_CR52","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.neucom.2014.12.123","volume":"173","author":"Q Zou","year":"2016","unstructured":"Zou, Q., Zeng, J., Cao, L., et al. (2016). A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing, 173, 346\u2013354. https:\/\/doi.org\/10.1016\/j.neucom.2014.12.123.","journal-title":"Neurocomputing"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06191-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-022-06191-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06191-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:02:43Z","timestamp":1688601763000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-022-06191-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,6]]},"references-count":52,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["6191"],"URL":"https:\/\/doi.org\/10.1007\/s10994-022-06191-y","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,6]]},"assertion":[{"value":"18 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All the authors declared that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Our source code\u00a0is available on\u00a0, along with detailed files and instructions to reproduce our results.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}