{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adversarial-regression-with-multiple-learners","title":"Adversarial Regression with Multiple Learners","arxiv_id":"1806.02256","date":"2018-06-06","proceeding":"ICML 2018 7","authors":["Liang Tong","Sixie Yu","Scott Alfeld","Yevgeniy Vorobeychik"],"abstract":"Despite the considerable success enjoyed by machine learning techniques in\npractice, numerous studies demonstrated that many approaches are vulnerable to\nattacks. An important class of such attacks involves adversaries changing\nfeatures at test time to cause incorrect predictions. Previous investigations\nof this problem pit a single learner against an adversary. However, in many\nsituations an adversary's decision is aimed at a collection of learners, rather\nthan specifically targeted at each independently. We study the problem of\nadversarial linear regression with multiple learners. We approximate the\nresulting game by exhibiting an upper bound on learner loss functions, and show\nthat the resulting game has a unique symmetric equilibrium. We present an\nalgorithm for computing this equilibrium, and show through extensive\nexperiments that equilibrium models are significantly more robust than\nconventional regularized linear regression.","url_abs":"http://arxiv.org/abs/1806.02256v1","url_pdf":"http://arxiv.org/pdf/1806.02256v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"adversarial-regression-with-multiple-learners","repo_url":"https://github.com/marsplus/Adversarial-Regression-with-Multiple-Learners","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"linear-regression","method_name":"Linear Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.02256","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}