TY - JOUR AU - Xie, Jiahao AU - Shen, Zebang AU - Zhang, Chao AU - Wang, Boyu AU - Qian, Hui PY - 2020/04/03 Y2 - 2024/03/28 TI - Efficient Projection-Free Online Methods with Stochastic Recursive Gradient JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 04 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v34i04.6116 UR - https://ojs.aaai.org/index.php/AAAI/article/view/6116 SP - 6446-6453 AB - <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.</p> ER -