{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:05:45Z","timestamp":1743073545747,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031539688"},{"type":"electronic","value":"9783031539695"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-53969-5_8","type":"book-chapter","created":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T09:03:35Z","timestamp":1707987815000},"page":"93-103","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Perceptrons Under Verifiable Random Data Corruption"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4074-3178","authenticated-orcid":false,"given":"Jose E. Aguilar","family":"Escamilla","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2934-606X","authenticated-orcid":false,"given":"Dimitrios I.","family":"Diochnos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"key":"8_CR1","unstructured":"Barocas, S., Hardt, M., Narayanan, A.: Fairness and machine learning: limitations and opportunities. fairmlbook.org (2019). http:\/\/www.fairmlbook.org"},{"key":"8_CR2","unstructured":"Baum, E.: The perceptron algorithm is fast for non-malicious distributions. In: NeurIPS 1989, vol. 2, pp. 676\u2013685. Morgan-Kaufmann (1989)"},{"key":"8_CR3","unstructured":"Biggio, B., Nelson, B., Laskov, P.: Poisoning attacks against support vector machines. In: ICML 2012. icml.cc\/Omnipress (2012)"},{"key":"8_CR4","unstructured":"Brown, T.B., et al.: Language models are few-shot learners. In: NeurIPS 2020, Virtual (2020)"},{"key":"8_CR5","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9780262170055.001.0001","volume-title":"Dataset Shift in Machine Learning","author":"J Qui\u00f1onero Candela","year":"2008","unstructured":"Qui\u00f1onero Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N.D.: Dataset Shift in Machine Learning. The MIT Press, Cambridge (2008)"},{"key":"8_CR6","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"issue":"2","key":"8_CR7","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s10994-009-5124-8","volume":"81","author":"O Dekel","year":"2010","unstructured":"Dekel, O., Shamir, O., Xiao, L.: Learning to classify with missing and corrupted features. Mach. Learn. 81(2), 149\u2013178 (2010)","journal-title":"Mach. Learn."},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Diochnos, D.I., Trafalis, T.B.: Learning reliable rules under class imbalance. In: SDM, pp. 28\u201336. SIAM (2021)","DOI":"10.1137\/1.9781611976700.4"},{"key":"8_CR9","doi-asserted-by":"publisher","unstructured":"Fellicious, C., Wei\u00dfgerber, T., Granitzer, M.: Effects of random seeds on the accuracy of convolutional neural networks. In: LOD 2020, Revised Selected Papers, Part II. LNCS, vol. 12566, pp. 93\u2013102. Springer, Heidelberg (2020). https:\/\/doi.org\/10.1007\/978-3-030-64580-9_8","DOI":"10.1007\/978-3-030-64580-9_8"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Flansburg, C., Diochnos, D.I.: Wind prediction under random data corruption (student abstract). In: AAAI 2022, pp. 12945\u201312946. AAAI Press (2022)","DOI":"10.1609\/aaai.v36i11.21609"},{"issue":"2","key":"8_CR11","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1109\/72.80230","volume":"1","author":"SI Gallant","year":"1990","unstructured":"Gallant, S.I.: Perceptron-based learning algorithms. IEEE Trans. Neural Netw. 1(2), 179\u2013191 (1990)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"2","key":"8_CR12","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/s00521-009-0295-6","volume":"19","author":"PJ Garc\u00eda-Laencina","year":"2010","unstructured":"Garc\u00eda-Laencina, P.J., Sancho-G\u00f3mez, J., Figueiras-Vidal, A.R.: Pattern classification with missing data: a review. Neural Comput. Appl. 19(2), 263\u2013282 (2010)","journal-title":"Neural Comput. Appl."},{"issue":"2","key":"8_CR13","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1109\/TPAMI.2022.3162397","volume":"45","author":"M Goldblum","year":"2023","unstructured":"Goldblum, M., et al.: Dataset security for machine learning: data poisoning, backdoor attacks, and defenses. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 1563\u20131580 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"8_CR14","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1145\/3134599","volume":"61","author":"IJ Goodfellow","year":"2018","unstructured":"Goodfellow, I.J., McDaniel, P.D., Papernot, N.: Making machine learning robust against adversarial inputs. Commun. ACM 61(7), 56\u201366 (2018)","journal-title":"Commun. ACM"},{"issue":"9","key":"8_CR15","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263\u20131284 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Impagliazzo, R., Lei, R., Pitassi, T., Sorrell, J.: Reproducibility in learning. In: STOC 2022, pp. 818\u2013831. ACM (2022)","DOI":"10.1145\/3519935.3519973"},{"issue":"4","key":"8_CR17","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1137\/0222052","volume":"22","author":"MJ Kearns","year":"1993","unstructured":"Kearns, M.J., Li, M.: Learning in the presence of malicious errors. SIAM J. Comput. 22(4), 807\u2013837 (1993)","journal-title":"SIAM J. Comput."},{"key":"8_CR18","unstructured":"Koh, P.W., Liang, P.: Understanding black-box predictions via influence functions. In: ICML 2017. Proceedings of Machine Learning Research, vol. 70, pp. 1885\u20131894. PMLR (2017)"},{"issue":"1","key":"8_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10994-021-06119-y","volume":"111","author":"PW Koh","year":"2022","unstructured":"Koh, P.W., Steinhardt, J., Liang, P.: Stronger data poisoning attacks break data sanitization defenses. Mach. Learn. 111(1), 1\u201347 (2022)","journal-title":"Mach. Learn."},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Krishnaswamy, A.K., Li, H., Rein, D., Zhang, H., Conitzer, V.: Classification with strategically withheld data. In: AAAI 2021, pp. 5514\u20135522. AAAI Press (2021)","DOI":"10.1609\/aaai.v35i6.16694"},{"key":"8_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4613-1685-5","volume-title":"Learning from Good and Bad Data","author":"PD Laird","year":"2012","unstructured":"Laird, P.D.: Learning from Good and Bad Data, vol. 47. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-1-4613-1685-5"},{"issue":"4","key":"8_CR22","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1145\/3583078","volume":"66","author":"G Marcus","year":"2023","unstructured":"Marcus, G.: Hoping for the best as AI evolves. Commun. ACM 66(4), 6\u20137 (2023). https:\/\/doi.org\/10.1145\/3583078","journal-title":"Commun. ACM"},{"key":"8_CR23","unstructured":"Molnar, C.: Interpretable Machine Learning, 2 edn. Independently Published, Chappaqua (2022). https:\/\/christophm.github.io\/interpretable-ml-book"},{"key":"8_CR24","volume-title":"Principles of Neurodynamics","author":"F Rosenblatt","year":"1962","unstructured":"Rosenblatt, F.: Principles of Neurodynamics. Spartan Books, New York (1962)"},{"issue":"5","key":"8_CR25","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206\u2013215 (2019)","journal-title":"Nat. Mach. Intell."},{"key":"8_CR26","unstructured":"Shafahi, A., et al.: Poison frogs! targeted clean-label poisoning attacks on neural networks. In: NeurIPS 2018, pp. 6106\u20136116 (2018)"},{"key":"8_CR27","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107298019","volume-title":"Understanding Machine Learning - From Theory to Algorithms","author":"S Shalev-Shwartz","year":"2014","unstructured":"Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning - From Theory to Algorithms. Cambridge University Press, Cambridge (2014)"},{"issue":"11","key":"8_CR28","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1145\/1968.1972","volume":"27","author":"LG Valiant","year":"1984","unstructured":"Valiant, L.G.: A theory of the learnable. Commun. ACM 27(11), 1134\u20131142 (1984)","journal-title":"Commun. ACM"},{"key":"8_CR29","volume-title":"Trustworthy Machine Learning","author":"KR Varshney","year":"2022","unstructured":"Varshney, K.R.: Trustworthy Machine Learning. Independently Published, Chappaqua (2022)"},{"key":"8_CR30","doi-asserted-by":"crossref","unstructured":"Vorobeychik, Y., Kantarcioglu, M.: Adversarial machine learning. In: Synthesis Lectures on Artificial Intelligence and Machine Learning, # 38. Morgan & Claypool, San Rafael (2018)","DOI":"10.1007\/978-3-031-01580-9"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53969-5_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T09:05:27Z","timestamp":1707987927000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53969-5_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031539688","9783031539695"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53969-5_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"16 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grasmere","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2023.icas.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"In-house system and EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"119","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"61% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5-6","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1-2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}