{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T20:25:49Z","timestamp":1787689549368,"version":"build-2784847793"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770854.3780243","type":"proceedings-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T12:07:40Z","timestamp":1785499660000},"page":"302-313","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Countering Overfitting with Counterfactual Examples"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6654-4879","authenticated-orcid":false,"given":"Flavio","family":"Giorgi","sequence":"first","affiliation":[{"name":"Sapienza University of Rome, Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1563-4953","authenticated-orcid":false,"given":"Fabiano","family":"Veglianti","sequence":"additional","affiliation":[{"name":"Sapienza University of Rome, Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7669-9055","authenticated-orcid":false,"given":"Fabrizio","family":"Silvestri","sequence":"additional","affiliation":[{"name":"Sapienza University of Rome, Rome, Italy and ISTI-CNR, Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7471-6659","authenticated-orcid":false,"given":"Gabriele","family":"Tolomei","sequence":"additional","affiliation":[{"name":"Sapienza University of Rome, Rome, Italy and ISTI-CNR, Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Unrestricted Adversarial Examples. arXiv preprint arXiv:1809.08352","author":"Brown Tom B","year":"2018","unstructured":"Tom B Brown, Nicholas Carlini, Chiyuan Zhang, Catherine Olsson, Paul Christiano, and Ian Goodfellow. 2018. Unrestricted Adversarial Examples. arXiv preprint arXiv:1809.08352 (2018)."},{"key":"e_1_3_2_2_2_1","volume-title":"Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping. In Advances in Neural Information Processing Systems","author":"Caruana Rich","year":"2000","unstructured":"Rich Caruana, Steve Lawrence, and C. Giles. 2000. Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping. In Advances in Neural Information Processing Systems, T. Leen, T. Dietterich, and V. Tresp (Eds.), Vol. 13. MIT Press, 381-387. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2000\/file\/059fdcd96baeb75112f09fa1dcc740cc-Paper.pdf"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3731683"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2022.3223892"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557429"},{"key":"e_1_3_2_2_6_1","unstructured":"Thomson-Sintra Dominique Van Cappel. 1993. Phoneme. https:\/\/www.openml.org\/search?type=data&sort=version&status=any&order=asc&exact_name=phoneme&id=1489."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11023-021-09580-9"},{"key":"e_1_3_2_2_8_1","first-page":"1","article-title":"Domain-Adversarial Training of Neural Networks","volume":"17","author":"Ganin Yaroslav","year":"2016","unstructured":"Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Fran\u00e7ois Laviolette, Mario Marchand, and Victor Lempitsky. 2016. Domain-Adversarial Training of Neural Networks. Journal of Machine Learning Research, Vol. 17, 1 (jan 2016), 2096-2030.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_9_1","volume-title":"Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research","volume":"3573","author":"Giorgi Flavio","year":"2025","unstructured":"Flavio Giorgi, Cesare Campagnano, Fabrizio Silvestri, and Gabriele Tolomei. 2025. Natural Language Counterfactual Explanations for Graphs Using Large Language Models. In Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 258), Yingzhen Li, Stephan Mandt, Shipra Agrawal, and Emtiyaz Khan (Eds.). PMLR, 3565-3573. https:\/\/proceedings.mlr.press\/v258\/giorgi25a.html"},{"key":"e_1_3_2_2_10_1","volume-title":"Local Rule-Based Explanations of Black Box Decision Systems. arXiv preprint arXiv:1805.10820","author":"Guidotti Riccardo","year":"2018","unstructured":"Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini, and Fosca Giannotti. 2018. Local Rule-Based Explanations of Black Box Decision Systems. arXiv preprint arXiv:1805.10820 (2018)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/3327546.3327617"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359789.3359824"},{"key":"e_1_3_2_2_14_1","volume-title":"7th International Conference on Learning Representations, ICLR 2019","author":"Hjelm R. Devon","year":"2019","unstructured":"R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Philip Bachman, Adam Trischler, and Yoshua Bengio. 2019. Learning Deep Representations by Mutual Information Estimation and Maximization. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net. https:\/\/openreview.net\/forum?id=Bklr3j0cKX"},{"key":"e_1_3_2_2_15_1","volume-title":"Gradient Descent Aligns the Layers of Deep Linear Networks. In 7th International Conference on Learning Representations, ICLR 2019","author":"Ji Ziwei","year":"2019","unstructured":"Ziwei Ji and Matus Telgarsky. 2019. Gradient Descent Aligns the Layers of Deep Linear Networks. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net. https:\/\/openreview.net\/forum?id=HJflg30qKX"},{"key":"e_1_3_2_2_16_1","unstructured":"Aditya Kadiwal. 2020. Water Potability Dataset. https:\/\/www.kaggle.com\/datasets\/adityakadiwal\/water-potability Accessed: 2025-01-27."},{"key":"e_1_3_2_2_17_1","volume-title":"Model-Agnostic Counterfactual Explanations for Consequential Decisions. In International Conference on Artificial Intelligence and Statistics. PMLR, 895-905","author":"Karimi Amir-Hossein","year":"2020","unstructured":"Amir-Hossein Karimi, Gilles Barthe, Borja Balle, and Isabel Valera. 2020. Model-Agnostic Counterfactual Explanations for Consequential Decisions. In International Conference on Artificial Intelligence and Statistics. PMLR, 895-905."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.870038"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1015362183"},{"key":"e_1_3_2_2_20_1","unstructured":"Alex Krizhevsky. 2009. Learning Multiple Layers of Features from Tiny Images. (2009) 32-33. https:\/\/www.cs.toronto.edu\/ kriz\/learning-features-2009-TR.pdf"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403066"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.15252\/msb.20199198"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i5.20468"},{"key":"e_1_3_2_2_24_1","volume-title":"Proceedings of The 25th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research","volume":"4511","author":"Lucic Ana","year":"2022","unstructured":"Ana Lucic, Maartje A. Ter Hoeve, Gabriele Tolomei, Maarten De Rijke, and Fabrizio Silvestri. 2022b. CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks. In Proceedings of The 25th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 151), Gustau Camps-Valls, Francisco J. R. Ruiz, and Isabel Valera (Eds.). PMLR, 4499-4511. https:\/\/proceedings.mlr.press\/v151\/lucic22a.html"},{"key":"e_1_3_2_2_25_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rJzIBfZAb","author":"Madry Aleksander","year":"2018","unstructured":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards Deep Learning Models Resistant to Adversarial Attacks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rJzIBfZAb"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2024.3496616"},{"key":"e_1_3_2_2_27_1","volume-title":"Proceedings of The 25th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research","volume":"4594","author":"Pawelczyk Martin","year":"2022","unstructured":"Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, and Himabindu Lakkaraju. 2022. Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. In Proceedings of The 25th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 151), Gustau Camps-Valls, Francisco J. R. Ruiz, and Isabel Valera (Eds.). PMLR, 4574-4594. https:\/\/proceedings.mlr.press\/v151\/pawelczyk22a.html"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/Allerton.2011.6120320"},{"key":"e_1_3_2_2_29_1","first-page":"1","article-title":"The Implicit Bias of Gradient Descent on Separable Data","volume":"19","author":"Soudry Daniel","year":"2018","unstructured":"Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, and Nathan Srebro. 2018. The Implicit Bias of Gradient Descent on Separable Data. J. Mach. Learn. Res., Vol. 19, 1 (Jan. 2018), 2822-2878.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3051315"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00714"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.244"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2945326"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098039"},{"key":"e_1_3_2_2_35_1","first-page":"841","article-title":"Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR","volume":"31","author":"Wachter Sandra","year":"2017","unstructured":"Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2017. Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR. Harvard Journal of Law & Technology., Vol. 31 (2017), 841-887.","journal-title":"Harvard Journal of Law & Technology."},{"key":"e_1_3_2_2_36_1","volume-title":"2nd International Conference on Learning Representations, ICLR","author":"Warde-Farley David","year":"2014","unstructured":"David Warde-Farley, Ian J. Goodfellow, Aaron C. Courville, and Yoshua Bengio. 2014. An Empirical Analysis of Dropout in Piecewise Linear Networks. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http:\/\/arxiv.org\/abs\/1312.6197"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","unstructured":"Daniel Whiteson. 2014. HIGGS. UCI Machine Learning Repository. DOI: https:\/\/doi.org\/10.24432\/C5V312.","DOI":"10.24432\/C5V312"},{"key":"e_1_3_2_2_38_1","unstructured":"Kaiwen Wu and Yaoliang Yu. 2019. Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin. arXiv:1907.11780 [cs.LG] https:\/\/arxiv.org\/abs\/1907.11780"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btn602"}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3770854.3780243","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T20:11:01Z","timestamp":1787688661000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770854.3780243"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,20]]},"references-count":39,"alternative-id":["10.1145\/3770854.3780243","10.1145\/3770854"],"URL":"https:\/\/doi.org\/10.1145\/3770854.3780243","relation":{},"subject":[],"published":{"date-parts":[[2026,4,20]]},"assertion":[{"value":"2026-04-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}