{"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/positive-unlabeled-learning-with-non-negative","title":"Positive-Unlabeled Learning with Non-Negative Risk Estimator","arxiv_id":"1703.00593","date":"2017-03-02","proceeding":"NeurIPS 2017 12","authors":["Ryuichi Kiryo","Gang Niu","Marthinus C. Du Plessis","Masashi Sugiyama"],"abstract":"From only positive (P) and unlabeled (U) data, a binary classifier could be\ntrained with PU learning, in which the state of the art is unbiased PU\nlearning. However, if its model is very flexible, empirical risks on training\ndata will go negative, and we will suffer from serious overfitting. In this\npaper, we propose a non-negative risk estimator for PU learning: when getting\nminimized, it is more robust against overfitting, and thus we are able to use\nvery flexible models (such as deep neural networks) given limited P data.\nMoreover, we analyze the bias, consistency, and mean-squared-error reduction of\nthe proposed risk estimator, and bound the estimation error of the resulting\nempirical risk minimizer. Experiments demonstrate that our risk estimator fixes\nthe overfitting problem of its unbiased counterparts.","url_abs":"http://arxiv.org/abs/1703.00593v2","url_pdf":"http://arxiv.org/pdf/1703.00593v2.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":"positive-unlabeled-learning-with-non-negative","repo_url":"https://github.com/kiryor/nnPUlearning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.00593","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}