{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T06:44:46Z","timestamp":1773384286421,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"04","license":[{"start":{"date-parts":[[2020,4,3]],"date-time":"2020-04-03T00:00:00Z","timestamp":1585872000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.aaai.org"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>This paper focuses on projection-free methods for solving smooth Online Convex Optimization (OCO) problems. Existing projection-free methods either achieve suboptimal regret bounds or have high per-round computational costs. To fill this gap, two efficient projection-free online methods called ORGFW and MORGFW are proposed for solving stochastic and adversarial OCO problems, respectively. By employing a recursive gradient estimator, our methods achieve optimal regret bounds (up to a logarithmic factor) while possessing low per-round computational costs. Experimental results demonstrate the efficiency of the proposed methods compared to state-of-the-arts.<\/jats:p>","DOI":"10.1609\/aaai.v34i04.6116","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T20:39:23Z","timestamp":1593463163000},"page":"6446-6453","source":"Crossref","is-referenced-by-count":8,"title":["Efficient Projection-Free Online Methods with Stochastic Recursive Gradient"],"prefix":"10.1609","volume":"34","author":[{"given":"Jiahao","family":"Xie","sequence":"first","affiliation":[]},{"given":"Zebang","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Boyu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Qian","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2020,4,3]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/6116\/5972","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/6116\/5972","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T00:18:27Z","timestamp":1667521107000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/6116"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,3]]},"references-count":0,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2020,6,16]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v34i04.6116","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2020,4,3]]}}}