[29] M. R. Qureshi, L. Galárraga and M. Couceiro. *A reinforcement learning approach to mitigating stereotypical biases in language models*. 15th Nov. 2023. URL: https://inria.hal.science/hal-04426115.

[30] A. Baril, M. Couceiro and V. Lagerkvist. 'Linear Bounds between Component Twin-Width and Clique-Width with Algorithmic Applications to Counting Graph Colorings'. working paper or preprint. Dec. 2023. URL: https://hal.science/hal-04142719.

## 11.3 Cited publications

[31] G. Alves, M. Amblard, F. Bernier, M. Couceiro and A. Napoli. 'Reducing Unintended Bias of ML Models on Tabular and Textual Data'. In: *DSAA 2021 - 8th IEEE International Conference on Data Science and Advanced Analytics*. Porto (virtual event), Portugal, Oct. 2021, pp. 1-10. URL: https://hal.science/hal-03312797.

[32] A. Baril, M. Couceiro and V. Lagerkvist. 'New Perspectives on Semiring Applications to Dynamic Programming'. working paper or preprint. Feb. 2024. URL: https://hal.science/hal-04434071.

[33] E. M. Bender, T. Gebru, A. McMillan-Major and S. Shmitchell. 'On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?' In: *FAccT '21: 2021 ACM Conference on Fairness, Accountability, and Transparency, Virtual Event / Toronto, Canada, March 3-10, 2021*. Ed. by M. C. Elish, W. Isaac and R. S. Zemel. ACM, 2021, pp. 610-623.

[34] V. Bhargava, M. Couceiro and A. Napoli. 'LimeOut: An Ensemble Approach To Improve Process Fairness'. In: *ECML-PKDD International Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2020)*. Vol. 1323. Communications in Computer and Information Science. Ghent, Belgium: Springer, Sept. 2020, pp. 475-491. URL: https://hal.science/hal-02979233.

[35] P. Delobelle, E. K. Tokpo, T. Calders and B. Berendt. 'Measuring Fairness with Biased Rulers: A Comparative Study on Bias Metrics for Pre-trained Language Models'. In: *Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, July 10-15, 2022*. Ed. by M. Carpuat, M. de Marneffe and I. V. M. Ruíz. Association for Computational Linguistics, 2022, pp. 1693-1706.

[36] R. Ganian, T. Hamm, V. Korchemna, K. Okrasa and K. Simonov. 'The Fine-Grained Complexity of Graph Homomorphism Parameterized by Clique-Width'. In: *49th International Colloquium on Automata, Languages, and Programming, ICALP 2022, July 4-8, 2022, Paris, France*. Ed. by M. Bojanczyk, E. Merelli and D. P. Woodruff. Vol. 229. LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022, 66:1-66:20. DOI: 10.4230/LIPICS.ICALP.2022.66. URL: https://doi.org/10.4230/LIPICS.ICALP.2022.66.

[37] Y. Gao, S. Zeng, X. Xu, H. Li, S. Yao, K. Song, X. Li, L. Chen, J. Tang, H. Xing et al. 'Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study.' In: *The Lancet. Digital health* 4 3 (2022), e179-e187. URL: https://api.semanticscholar.org/CorpusID:247089260.

[38] S. Goldfarb-Tarrant, R. Marchant, R. M. Sánchez, M. Pandya and A. Lopez. 'Intrinsic Bias Metrics Do Not Correlate with Application Bias'. In: *Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers)*, Virtual Event, August 1-6, 2021. Ed. by C. Zong, F. Xia, W. Li and R. Navigli. Association for Computational Linguistics, 2021, pp. 1926-1940.

[39] P. Jensen, L. Jensen and S. Brunak. 'Mining electronic health records: towards better research applications and clinical care.' English. In: *Nature Reviews. Genetics* 13.6 (2012), pp. 395-405. DOI: 10.1038/nrg3208.

[40] I. Landi, B. S. Glicksberg, H. Lee, S. T. Cherng, G. Landi, M. Danieletto, J. T. Dudley, C. Furlanello and R. Miotto. 'Deep representation learning of electronic health records to unlock patient stratification at scale'. In: *npj Digit. Medicine* 3 (2020). DOI: 10.1038/S41746-020-0301-Z. URL: https://doi.org/10.1038/s41746-020-0301-z.
[29] M. R. Qureshi, L. Galárraga and M. Couceiro. *A reinforcement learning approach to mitigating stereotypical biases in language models*. 15th Nov. 2023. URL: https://inria.hal.science/hal-04426115.

[30] A. Baril, M. Couceiro and V. Lagerkvist. 'Linear Bounds between Component Twin-Width and Clique-Width with Algorithmic Applications to Counting Graph Colorings'. working paper or preprint. Dec. 2023. URL: https://hal.science/hal-04142719.

## 11.3 Cited publications

[31] G. Alves, M. Amblard, F. Bernier, M. Couceiro and A. Napoli. 'Reducing Unintended Bias of ML Models on Tabular and Textual Data'. In: *DSAA 2021 - 8th IEEE International Conference on Data Science and Advanced Analytics*. Porto (virtual event), Portugal, Oct. 2021, pp. 1-10. URL: https://hal.science/hal-03312797.

[32] A. Baril, M. Couceiro and V. Lagerkvist. 'New Perspectives on Semiring Applications to Dynamic Programming'. working paper or preprint. Feb. 2024. URL: https://hal.science/hal-04434071.

[33] E. M. Bender, T. Gebru, A. McMillan-Major and S. Shmitchell. 'On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?' In: *FACCT '21: 2021 ACM Conference on Fairness, Accountability, and Transparency, Virtual Event / Toronto, Canada, March 3-10, 2021*. Ed. by M. C. Elish, W. Isaac and R. S. Zemel. ACM, 2021, pp. 610-623.

[34] V. Bhargava, M. Couceiro and A. Napoli. 'LimeOut: An Ensemble Approach To Improve Process Fairness'. In: *ECML-PKDD International Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2020)*. Vol. 1323. Communications in Computer and Information Science. Ghent, Belgium: Springer, Sept. 2020, pp. 475-491. URL: https://hal.science/hal-02979233.

[35] P. Delobelle, E. K. Tokpo, T. Calders and B. Berendt. 'Measuring Fairness with Biased Rulers: A Comparative Study on Bias Metrics for Pre-trained Language Models'. In: *Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, July 10-15, 2022*. Ed. by M. Carpuat, M. de Marneffe and I. V. M. Ruíz. Association for Computational Linguistics, 2022, pp. 1693-1706.

[36] R. Ganian, T. Hamm, V. Korchemna, K. Okrasa and K. Simonov. 'The Fine-Grained Complexity of Graph Homomorphism Parameterized by Clique-Width'. In: *49th International Colloquium on Automata, Languages, and Programming, ICALP 2022, July 4-8, 2022, Paris, France*. Ed. by M. Bojanczyk, E. Merelli and D. P. Woodruff. Vol. 229. LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022, 66:1-66:20. DOI: 10.4230/LIPICS.ICALP.2022.66. URL: https://doi.org/10.4230/LIPICS.ICALP.2022.66.

[37] Y. Gao, S. Zeng, X. Xu, H. Li, S. Yao, K. Song, X. Li, L. Chen, J. Tang, H. Xing et al. 'Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study.' In: *The Lancet. Digital health* 4 3 (2022), e179-e187. URL: https://api.semanticscholar.org/CorpusID:247089260.

[38] S. Goldfarb-Tarrant, R. Marchant, R. M. Sánchez, M. Pandya and A. Lopez. 'Intrinsic Bias Metrics Do Not Correlate with Application Bias'. In: *Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers)*, Virtual Event, August 1-6, 2021. Ed. by C. Zong, F. Xia, W. Li and R. Navigli. Association for Computational Linguistics, 2021, pp. 1926-1940.

[39] P. Jensen, L. Jensen and S. Brunak. 'Mining electronic health records: towards better research applications and clinical care.' English. In: *Nature Reviews. Genetics* 13.6 (2012), pp. 395-405. DOI: 10.1038/nrg3208.

[40] I. Landi, B. S. Glicksberg, H. Lee, S. T. Cherng, G. Landi, M. Danieletto, J. T. Dudley, C. Furlanello and R. Miotto. 'Deep representation learning of electronic health records to unlock patient stratification at scale'. In: *npj Digit. Medicine* 3 (2020). DOI: 10.1038/S41746-020-0301-Z. URL: https://doi.org/10.1038/s41746-020-0301-z.
[41] T. Li, D. Khashabi, T. Khot, A. Sabharwal and V. Srikumar. 'UNQOVERing Stereotyping Biases via Underspecified Questions'. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Online: Association for Computational Linguistics, Nov. 2020, pp. 3475-3489. DOI: 10.18653/v1/2020.findings-emnlp.311. URL: https://aclanthology.org/2020.findings-emnlp.311.

[42] C. Rudin, C. Chen, Z. Chen, H. Huang, L. Semenova and C. Zhong. 'Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges'. In: CoRR abs/2103.11251 (2021). arXiv: 2103.11251. URL: https://arxiv.org/abs/2103.11251.

[43] A. Schuler, A. Callahan, K. Jung and N. H. Shah. 'Performing an Informatics Consult: Methods and Challenges'. In: Journal of the American College of Radiology 15.3, Part B (2018). Data Science: Big Data Machine Learning and Artificial Intelligence, pp. 563-568. DOI: https://doi.org/10.1016/j.jacr.2017.12.023. URL: https://www.sciencedirect.com/science/article/pii/S154614401731668X.

[44] Y. Si, J. Du, Z. Li, X. Jiang, T. A. Miller, F. Wang, W. J. Zheng and K. Roberts. 'Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review'. In: J. Biomed. Informatics 115 (2021), p. 103671. DOI: 10.1016/J.JBI.2020.103671. URL: https://doi.org/10.1016/j.jbi.2020.103671.

[45] C. Theobald, F. Pennerath, B. Conan-Guez, M. Couceiro and A. Napoli. Clarity: an improved gradient method for producing quality visual counterfactual explanations. 2022. arXiv: 2211.15370 [cs.CV].

[46] G. Zervakis, E. Vincent, M. Couceiro, M. Schoenauer and E. Marquer. 'An analogy based approach for solving target sense verification'. In: NLPIR 2022 - 6th International Conference on Natural Language Processing and Information Retrieval. Bangkok, Thailand, Dec. 2022. URL: https://inria.hal.science/hal-03792071.
[41] T. Li, D. Khashabi, T. Khot, A. Sabharwal and V. Srikumar. 'UNQOVERing Stereotyping Biases via Underspecified Questions'. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Online: Association for Computational Linguistics, Nov. 2020, pp. 3475-3489. DOI: 10.18653/v1/2020.findings-emnlp.311. URL: https://aclanthology.org/2020.findings-emnlp.311.

[42] C. Rudin, C. Chen, Z. Chen, H. Huang, L. Semenova and C. Zhong. 'Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges'. In: CoRR abs/2103.11251 (2021). arXiv: 2103.11251. URL: https://arxiv.org/abs/2103.11251.

[43] A. Schuler, A. Callahan, K. Jung and N. H. Shah. 'Performing an Informatics Consult: Methods and Challenges'. In: Journal of the American College of Radiology 15.3, Part B (2018). Data Science: Big Data Machine Learning and Artificial Intelligence, pp. 563-568. DOI: https://doi.org/10.1016/j.jacr.2017.12.023. URL: https://www.sciencedirect.com/science/article/pii/S154614401731668X.

[44] Y. Si, J. Du, Z. Li, X. Jiang, T. A. Miller, F. Wang, W. J. Zheng and K. Roberts. 'Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review'. In: J. Biomed. Informatics 115 (2021), p. 103671. DOI: 10.1016/J.JBI.2020.103671. URL: https://doi.org/10.1016/j.jbi.2020.103671.

[45] C. Theobald, F. Pennerath, B. Conan-Guez, M. Couceiro and A. Napoli. Clarity: an improved gradient method for producing quality visual counterfactual explanations. 2022. arXiv: 2211.15370 [cs.CV].

[46] G. Zervakis, E. Vincent, M. Couceiro, M. Schoenauer and E. Marquer. 'An analogy based approach for solving target sense verification'. In: NLPIR 2022 - 6th International Conference on Natural Language Processing and Information Retrieval. Bangkok, Thailand, Dec. 2022. URL: https://inria.hal.science/hal-03792071.