[48] W. Tang, S. Bi, and Y. Zhang. Online coordinated charging decision algorithm for electric vehicles without future information. IEEE Trans. Smart Grid, 5:2810-2824, May 2014.
[49] 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.
[50] Tongxin Li, Ruixiao Yang, Guannan Qu, Yiheng Lin, Steven Low, and Adam Wierman. Equipping black-box policies with model-based advice for stable nonlinear control. In https://arxiv.org/abs/2206.01341, 2022.
[51] Chris Develder, Nasrin Sadeghianpourhamami, Matthias Strobbe, and Nazir Refa. Quantifying flexibility in ev charging as dr potential: Analysis of two real-world data sets. In 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), pages 600-605. IEEE, 2016.
[52] Mohammad Ali Alomrani, Reza Moravej, and Elias B. Khalil. Deep policies for online bipartite matching: A reinforcement learning approach. CoRR, abs/2109.10380, 2021.
[53] Bingqian Du, Zhiyi Huang, and Chuan Wu. Adversarial deep learning for online resource allocation. ACM Trans. Model. Perform. Eval. Comput. Syst., 6(4), feb 2022.
[54] Gautam Goel, Yiheng Lin, Haoyuan Sun, and Adam Wierman. Beyond online balanced descent: An optimal algorithm for smoothed online optimization. In NeurIPS, volume 32, 2019.
[55] Pengfei Li, Jianyi Yang, and Shaolei Ren. Expert-calibrated learning for online optimization with switching costs. Proc. ACM Meas. Anal. Comput. Syst., 6(2), Jun 2022.
[56] Antonios Antoniadis, Christian Coester, Marek Elias, Adam Polak, and Bertrand Simon. Online metric algorithms with untrusted predictions. In ICML, 2020.
[57] Nicolas Christianson, Tinashe Handina, and Adam Wierman. Chasing convex bodies and functions with black-box advice. In  $COLT$ , 2022.
[58] Daan Rutten, Nico Christianson, Debankur Mukherjee, and Adam Wierman. Online optimization with untrusted predictions. CoRR, abs/2202.03519, 2022.
[59] Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, and Adam Wierman. Online optimization with memory and competitive control. In NeurIPS, volume 33. Curran Associates, Inc., 2020.
[60] Peter L Bartlett and Shahar Mendelson. Rademacher and gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research, 3(Nov):463-482, 2002.
[61] Martin Wainwright. Rademacher and empirical covering. https://people.eecs.berkeley.edu/~wainwrig/stat241b/lec20.pdf, 2009.
[62] Constantine Caramanis and Sujay Sanghavi. Projection onto a convex set. https://users.ece.utexas.edu/~cmcaram/EE381V_2012F/Lecture_3_Scribe_Notes.final.pdf, 2012.
[63] Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky. Spectrally-normalized margin bounds for neural networks. Advances in neural information processing systems, 30, 2017.
[64] Ian Goodfellow, *Yoshua Bengio*, and Aaron Courville. *Deep Learning*. MIT Press, 2016.
[65] Hyung Ju Suh, Max Simchowitz, Kaiqing Zhang, and Russ Tedrake. Do differentiable simulators give better policy gradients? In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 20668-20696. PMLR, 17-23 Jul 2022.
[66] S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
[67] Brandon Amos. Tutorial on amortized optimization for learning to optimize over continuous domains. CoRR, abs/2202.00665, 2022.
[68] Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, and J. Zico Kolter. Differentiable convex optimization layers. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019.