[23] Rishi Gupta and Tim Roughgarden. Data-driven algorithm design. Commun. ACM, 63(6):87-94, May 2020.
[24] Joan Boyar, Lene M. Favrholdt, Christian Kudahl, Kim S. Larsen, and Jesper W. Mikkelsen. Online algorithms with advice: A survey. SIGACT News, 47(3):93-129, August 2016.
[25] Pengfei Li, Jianyi Yang, and Shaolei Ren. Robustified learning for online optimization with memory costs. In INFOCOM, 2023.
[26] Weici Pan, Guanya Shi, Yiheng Lin, and Adam Wierman. Online optimization with feedback delay and nonlinear switching cost. Proc. ACM Meas. Anal. Comput. Syst., 6(1), Feb 2022.
[27] Martin Zinkevich. Online convex programming and generalized infinitesimal gradient ascent. In Proceedings of the 20th international conference on machine learning (icml-03), pages 928–936, 2003.
[28] Niangjun Chen, Joshua Comden, Zhenhua Liu, Anshul Gandhi, and Adam Wierman. Using predictions in online optimization: Looking forward with an eye on the past. SIGMETRICS Perform. Eval. Rev., 44(1):193-206, June 2016.
[29] Yingying Li, Guannan Qu, and Na Li. Online optimization with predictions and switching costs: Fast algorithms and the fundamental limit. IEEE Transactions on Automatic Control, 2020.
[30] Weiwei Kong, Christopher Liaw, Aranyak Mehta, and D. Sivakumar. A new dog learns old tricks: RL finds classic optimization algorithms. In ICLR, 2019.
[31] Mohammad Ali Alomrani, Reza Moravej, and Elias Boutros Khalil. Deep policies for online bipartite matching: A reinforcement learning approach. Transactions on Machine Learning Research, 2022.
[32] Bingqian Du, Chuan Wu, and Zhiyi Huang. Learning resource allocation and pricing for cloud profit maximization. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, AAAI'19/IAAI'19/EAAI'19. AAAI Press, 2019.
[33] Thodoris Lykouris and Sergei Vassilvitskii. Competitive caching with machine learned advice. J. ACM, 68(4), July 2021.
[34] Keerti Anand, Rong Ge, Amit Kumar, and Debmalya Panigrahi. A regression approach to learning-augmented online algorithms. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021.
[35] Pengfei Li, Jianyi Yang, and Shaolei Ren. Expert-calibrated learning for online optimization with switching costs. In SIGMETRICS, 2022.
[36] Sascha Geulen, Berthold Vöcking, and Melanie Winkler. Regret minimization for online buffering problems using the weighted majority algorithm. In  $COLT$ , pages 132-143. CiteSeer, 2010.
[37] Amir Sani, Gergely Neu, and Alessandro Lazaric. Exploiting easy data in online optimization. In Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems, volume 27. Curran Associates, Inc., 2014.
[38] Vaidehi Srinivas, David P. Woodruff, Ziyu Xu, and Samson Zhou. Memory bounds for the experts problem. In Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2022, page 1158-1171, New York, NY, USA, 2022. Association for Computing Machinery.
[39] Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, and Simon Shaolei Du. A reduction-based framework for conservative bandits and reinforcement learning. In International Conference on Learning Representations, 2022.
[40] Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, and Peter J. Ramadge. Projection-based constrained policy optimization. In International Conference on Learning Representations, 2020.
[41] Yecheng Jason Ma, Dinesh Jayaraman, and Osbert Bastani. Conservative offline distributional reinforcement learning. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021.