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Motivated by this, we study Online Convex Optimization with a switching limit, incorporating feedback delays and predictions. In this extended abstract, we established a near-optimal regret of O(T\/S) for delayed feedbacks and a bound of O(T\/S - t ) for predictions of t rounds even though the player is only allowed to move at most S times, in expectation, across T rounds. We developed an algorithm which achieves the bounds in both cases and still works when there are both delays and predictions.<\/jats:p>","DOI":"10.1145\/3626570.3626573","type":"journal-article","created":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T22:16:57Z","timestamp":1696285017000},"page":"3-5","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Switching Constrained Online Convex Optimization with Predictions and Feedback Delays"],"prefix":"10.1145","volume":"51","author":[{"given":"Weici","family":"Pan","sequence":"first","affiliation":[{"name":"Stony Brook University"}]},{"given":"Zhenhua","family":"Liu","sequence":"additional","affiliation":[{"name":"Stony Brook University"}]}],"member":"320","published-online":{"date-parts":[[2023,10,2]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2015.7403279"},{"key":"e_1_2_1_2_1","first-page":"3477","article-title":"Minimax regret of switching-constrained online convex optimization: No phase transition","volume":"33","author":"Chen L.","year":"2020","unstructured":"L. Chen, Q. Yu, H. Lawrence, and A. Karbasi. Minimax regret of switching-constrained online convex optimization: No phase transition. Advances in Neural Information Processing Systems, 33:3477--3486, 2020.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2015.2428253"},{"key":"e_1_2_1_4_1","volume-title":"H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00b4e-Buc","author":"Goel G.","year":"2019","unstructured":"G. Goel, Y. Lin, H. Sun, and A. Wierman. Beyond online balanced descent: An optimal algorithm for smoothed online optimization. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00b4e-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3512929"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2011.2112314"},{"key":"e_1_2_1_7_1","first-page":"3972","volume-title":"Conference on Learning Theory","author":"Sherman U.","year":"2021","unstructured":"U. Sherman and T. Koren. Lazy oco: Online convex optimization on a switching budget. In Conference on Learning Theory, pages 3972--3988. PMLR, 2021."},{"key":"e_1_2_1_8_1","first-page":"1994","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Yu C.","year":"2020","unstructured":"C. Yu, G. Shi, S.-J. Chung, Y. Yue, and A. Wierman. The power of predictions in online control. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 1994--2004. Curran Associates, Inc., 2020."}],"container-title":["ACM SIGMETRICS Performance Evaluation Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626570.3626573","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3626570.3626573","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:45Z","timestamp":1750178205000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626570.3626573"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"references-count":8,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,9,28]]}},"alternative-id":["10.1145\/3626570.3626573"],"URL":"https:\/\/doi.org\/10.1145\/3626570.3626573","relation":{},"ISSN":["0163-5999"],"issn-type":[{"type":"print","value":"0163-5999"}],"subject":[],"published":{"date-parts":[[2023,9,28]]},"assertion":[{"value":"2023-10-02","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}