{"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/regularized-learning-for-domain-adaptation-1","title":"Regularized Learning for Domain Adaptation under Label Shifts","arxiv_id":"1903.09734","date":"2019-03-22","proceeding":"ICLR 2019 5","authors":["Kamyar Azizzadenesheli","Anqi Liu","Fanny Yang","Animashree Anandkumar"],"abstract":"We propose Regularized Learning under Label shifts (RLLS), a principled and a\npractical domain-adaptation algorithm to correct for shifts in the label\ndistribution between a source and a target domain. We first estimate importance\nweights using labeled source data and unlabeled target data, and then train a\nclassifier on the weighted source samples. We derive a generalization bound for\nthe classifier on the target domain which is independent of the (ambient) data\ndimensions, and instead only depends on the complexity of the function class.\nTo the best of our knowledge, this is the first generalization bound for the\nlabel-shift problem where the labels in the target domain are not available.\nBased on this bound, we propose a regularized estimator for the small-sample\nregime which accounts for the uncertainty in the estimated weights. Experiments\non the CIFAR-10 and MNIST datasets show that RLLS improves classification\naccuracy, especially in the low sample and large-shift regimes, compared to\nprevious methods.","url_abs":"http://arxiv.org/abs/1903.09734v1","url_pdf":"http://arxiv.org/pdf/1903.09734v1.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":"regularized-learning-for-domain-adaptation-1","repo_url":"https://github.com/Angela0428/labelshift","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"regularized-learning-for-domain-adaptation-1","repo_url":"https://github.com/Angie-Liu/labelshift","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.09734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.09734"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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