{"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/target-contrastive-pessimistic-risk-for","title":"Target contrastive pessimistic risk for robust domain adaptation","arxiv_id":"1706.08082","date":"2017-06-25","proceeding":null,"authors":["Wouter M. Kouw","Marco Loog"],"abstract":"In domain adaptation, classifiers with information from a source domain adapt\nto generalize to a target domain. However, an adaptive classifier can perform\nworse than a non-adaptive classifier due to invalid assumptions, increased\nsensitivity to estimation errors or model misspecification. Our goal is to\ndevelop a domain-adaptive classifier that is robust in the sense that it does\nnot rely on restrictive assumptions on how the source and target domains relate\nto each other and that it does not perform worse than the non-adaptive\nclassifier. We formulate a conservative parameter estimator that only deviates\nfrom the source classifier when a lower risk is guaranteed for all possible\nlabellings of the given target samples. We derive the classical least-squares\nand discriminant analysis cases and show that these perform on par with\nstate-of-the-art domain adaptive classifiers in sample selection bias settings,\nwhile outperforming them in more general domain adaptation settings.","url_abs":"http://arxiv.org/abs/1706.08082v1","url_pdf":"http://arxiv.org/pdf/1706.08082v1.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":"target-contrastive-pessimistic-risk-for","repo_url":"https://github.com/wmkouw/libTLDA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"selection-bias","task_name":"Selection bias"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}