{"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/online-within-online-meta-learning","title":"Online-Within-Online Meta-Learning","arxiv_id":null,"date":"2019-12-01","proceeding":"NeurIPS 2019 12","authors":["Giulia Denevi","Dimitris Stamos","Carlo Ciliberto","Massimiliano Pontil"],"abstract":"We study the problem of learning a series of tasks in a fully online Meta-Learning\nsetting. The goal is to exploit similarities among the tasks to incrementally adapt\nan inner online algorithm in order to incur a low averaged cumulative error over\nthe tasks. We focus on a family of inner algorithms based on a parametrized\nvariant of online Mirror Descent. The inner algorithm is incrementally adapted\nby an online Mirror Descent meta-algorithm using the corresponding within-task\nminimum regularized empirical risk as the meta-loss. In order to keep the process\nfully online, we approximate the meta-subgradients by the online inner algorithm.\nAn upper bound on the approximation error allows us to derive a cumulative\nerror bound for the proposed method. Our analysis can also be converted to the\nstatistical setting by online-to-batch arguments. We instantiate two examples of the\nframework in which the meta-parameter is either a common bias vector or feature\nmap. Finally, preliminary numerical experiments confirm our theoretical findings.","url_abs":"http://papers.nips.cc/paper/9468-online-within-online-meta-learning","url_pdf":"http://papers.nips.cc/paper/9468-online-within-online-meta-learning.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":"online-within-online-meta-learning","repo_url":"https://github.com/dstamos/Adversarial-LTL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}