{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T08:51:20Z","timestamp":1777107080828,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":18,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,14]]},"DOI":"10.1145\/3787279.3787281","type":"proceedings-article","created":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T07:38:47Z","timestamp":1777102727000},"page":"6-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Adversarial Robustness"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1835-9225","authenticated-orcid":false,"given":"Tameem","family":"Adel","sequence":"first","affiliation":[{"name":"Data Science Dept. Department, National Physical Laboratory (NPL), Cambridge, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,25]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"T. Adel and M. Levene. 2023. A general model for side information in neural networks. Algorithms 16 (2023).","DOI":"10.3390\/a16110526"},{"key":"e_1_3_3_1_3_2","unstructured":"S. Borgeaud A. Mensch J. Hoffmann T. Cai E. Rutherford K. Millican G. van\u00a0den Driessche J. Lespiau B. Damoc and A. Clark. 2022. Improving language models by retrieving from trillions of tokens. International Conference on Machine Learning (ICML) (2022)."},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"J. Chen X. Liu and S. Lyu. 2012. Boosting with side information. Asian Conference on Computer Vision (2012).","DOI":"10.1007\/978-3-642-37331-2_43"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"crossref","unstructured":"S. Cuomo V.\u00a0Di Cola F. Giampaolo G. Rozza M. Raissi and F. Piccialli. 2022. Scientific machine learning through physics\u2013informed neural networks: Where we are and what is next. Journal of Scientific Computing (2022).","DOI":"10.1007\/s10915-022-01939-z"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"T. Dash S. Chitlangia A. Ahuja and A. Srinivasan. 2022. A review of some techniques for inclusion of domain-knowledge into deep neural networks. Nature Scientific Reports (2022).","DOI":"10.1038\/s41598-021-04590-0"},{"key":"e_1_3_3_1_7_2","unstructured":"Y. Ganin and V. Lempitsky. 2015. Unsupervised domain adaptation by backpropagation adversarial nets. International Conference on Machine Learning (ICML) (2015)."},{"key":"e_1_3_3_1_8_2","unstructured":"I. Goodfellow J. Pouget-Abadie M. Mirza B. Xu D. Warde-Farley S. Ozair A. Courville and Y. Bengio. 2014. Generative adversarial nets. Advances in Neural Information Processing Systems (NIPS) (2014) 2672\u20132680."},{"key":"e_1_3_3_1_9_2","unstructured":"A. Ibias K. Capala V. Varma A. Drozdz and J. Sousa. 2024. Improving noise robustness through abstractions and its impact on machine learning. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2406.08428 (2024)."},{"key":"e_1_3_3_1_10_2","unstructured":"S. Ishii and D. Ljunggren. 2021. A comparative analysis of robustness to noise in machine learning classifiers. Digitala Vetenskapliga Arkivet (2021)."},{"key":"e_1_3_3_1_11_2","unstructured":"D. Kingma and J. Ba. 2015. Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (ICLR) (2015)."},{"key":"e_1_3_3_1_12_2","unstructured":"J. Larson S. Mattu L. Kirchner and J. Angwin. 2016. https:\/\/github.com\/propublica\/compas-analysis. (2016)."},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"crossref","unstructured":"M. McCrory and S. Thomas. 2025. Cluster metric sensitivity to irrelevant features. Computational Problems in Science and Engineering II (2025).","DOI":"10.1007\/978-3-031-78416-3_7"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"S. Monaco D. Apiletti and G. Malnati. 2022. Theory-guided deep learning algorithms: An experimental evaluation. Electronics (2022).","DOI":"10.3390\/electronics11182850"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"D. Pessach and E. Shmueli. 2022. A review on fairness in machine learning. ACM Computing Surveys (CSUR) (2022).","DOI":"10.1145\/3494672"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"S. Shekhar and L. Akoglu. 2019. Incorporating privileged information to unsupervised anomaly detection. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) (2019).","DOI":"10.1007\/978-3-030-10925-7_6"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"crossref","unstructured":"V. Vapnik and A. Vashist. 2009. A new learning paradigm: Learning using privileged information general model for side information in neural networks. Neural Networks (2009).","DOI":"10.1016\/j.neunet.2009.06.042"},{"key":"e_1_3_3_1_18_2","unstructured":"Y. Wen G. Jerfel Muller M. Dusenberry J. Snoek B. Lakshminarayanan and D. Tran. 2020. Combining ensembles and data augmentation can harm your calibration. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2010.09875 (2020)."},{"key":"e_1_3_3_1_19_2","unstructured":"M. Zafar I. Valera M. Rodriguez and K. Gummadi. 2017. From parity to preference-based notions of fairness in classification. Advances in Neural Information Processing Systems (NIPS) (2017) (2017)."}],"event":{"name":"ICAAI 2025: 2025 9th International Conference on Advances in Artificial Intelligence","location":"Manchester United Kingdom","acronym":"ICAAI 2025"},"container-title":["Proceedings of the 2025 9th International Conference on Advances in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3787279.3787281","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T08:23:11Z","timestamp":1777105391000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3787279.3787281"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,14]]},"references-count":18,"alternative-id":["10.1145\/3787279.3787281","10.1145\/3787279"],"URL":"https:\/\/doi.org\/10.1145\/3787279.3787281","relation":{},"subject":[],"published":{"date-parts":[[2025,11,14]]},"assertion":[{"value":"2026-04-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}