{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T16:50:21Z","timestamp":1743267021347,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031264184"},{"type":"electronic","value":"9783031264191"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-26419-1_19","type":"book-chapter","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T00:24:57Z","timestamp":1679876697000},"page":"311-326","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Penalized FTRL with\u00a0Time-Varying Constraints"],"prefix":"10.1007","author":[{"given":"Douglas J.","family":"Leith","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Iosifidis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Anderson, D., Iosifidis, G., Leith, D.J.: Lazy lagrangians for optimistic learning With budget constraints. IEEE ACM Trans. Netw. (2023). IEEE","DOI":"10.1109\/TNET.2022.3222404"},{"issue":"24","key":"19_CR2","doi-asserted-by":"publisher","first-page":"6350","DOI":"10.1109\/TSP.2017.2750109","volume":"65","author":"T Chen","year":"2017","unstructured":"Chen, T., Ling, Q., Giannakis, G.B.: An online convex optimization approach to proactive network resource allocation. IEEE Trans. Signal Process. 65(24), 6350\u20136364 (2017)","journal-title":"IEEE Trans. Signal Process."},{"key":"19_CR3","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1561\/2400000013","volume":"2","author":"E Hazan","year":"2016","unstructured":"Hazan, E.: Introduction to online convex optimization. Found. Trends Optim. 2, 157\u2013325 (2016)","journal-title":"Found. Trends Optim."},{"key":"19_CR4","unstructured":"Jenatton, R., Huang, J.C., Archambeau, C.: Adaptive algorithms for online convex optimization with long-term constraints. In: Proceedings of ICML, pp. 402\u2013411 (2016)"},{"key":"19_CR5","unstructured":"Liakopoulos, N., Destounis, A., Paschos, G., Spyropoulos, T., Mertikopoulos, P.: Cautious regret minimization: online optimization with long-term budget constraints. In: Proceedings of ICML, pp. 3944\u20133952 (2019)"},{"issue":"81","key":"19_CR6","first-page":"2503","volume":"13","author":"M Mahdavi","year":"2012","unstructured":"Mahdavi, M., Jin, R., Yang, T.: Trading regret for efficiency: online convex optimization with long term constraints. J. Mach. Learn. Res. 13(81), 2503\u20132528 (2012)","journal-title":"J. Mach. Learn. Res."},{"issue":"20","key":"19_CR7","first-page":"569","volume":"10","author":"S Mannor","year":"2009","unstructured":"Mannor, S., Tsitsiklis, J.N., Yu, J.Y.: Online learning with sample path constraints. J. Mach. Learn. Res. 10(20), 569\u2013590 (2009)","journal-title":"J. Mach. Learn. Res."},{"key":"19_CR8","first-page":"1","volume":"18","author":"HB McMahan","year":"2017","unstructured":"McMahan, H.B.: A survey of algorithms and analysis for adaptive online learning. J. Mach. Learn. Res. 18, 1\u201350 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"19_CR9","first-page":"107","volume":"4","author":"S Shalev-Shwartz","year":"2011","unstructured":"Shalev-Shwartz, S.: Online learning and online convex optimization. Found. Trends Optim. 4, 107\u2013194 (2011)","journal-title":"Found. Trends Optim."},{"key":"19_CR10","unstructured":"Sun, W., Dey, D., Kapoor, A.: Safety-aware algorithms for adversarial contextual bandit. In: Proceedings of ICML, pp. 3280\u20133288 (2017)"},{"key":"19_CR11","unstructured":"Valls, V., Iosifidis, G., Leith, D., Tassiulas, L.: Online convex optimization with perturbed constraints: optimal rates against stronger benchmarks. In: Proceedings of AISTATS, pp. 2885\u20132895 (2020)"},{"issue":"5","key":"19_CR12","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1287\/mnsc.13.5.344","volume":"13","author":"WJ Zangwill","year":"1967","unstructured":"Zangwill, W.J.: Nonlinear programming via penalty functions. Manag. Sci. 13(5), 344\u2013358 (1967)","journal-title":"Manag. Sci."},{"key":"19_CR13","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1109\/TSP.2020.2964200","volume":"68","author":"X Yi","year":"2020","unstructured":"Yi, X., Li, X., Xie, L., Johansson, K.H.: Distributed online convex optimization with time-varying coupled inequality constraints. IEEE Trans. Signal Process. 68, 731\u2013746 (2020)","journal-title":"IEEE Trans. Signal Process."},{"key":"19_CR14","unstructured":"Yu, H., Nelly, M., Wei, X.: Online convex optimization with stochastic constraints. In: Proceedings of NIPS (2017)"},{"key":"19_CR15","unstructured":"Zinkevich, M.: Online convex programming and generalized infinitesimal gradient ascent. In: Proceedings of ICML (2003)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26419-1_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T00:31:16Z","timestamp":1679877076000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26419-1_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031264184","9783031264191"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26419-1_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1060","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"236","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17 demo track papers have been accepted from 28 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}