[26] Mohammad Mahdian, Hamid Nazerzadeh, and Amin Saberi. Online optimization with uncertain information. ACM Transactions on Algorithms (TALG), 8(1):1-29, 2012.
[27] Dhruv Rohatgi. Near-optimal bounds for online caching with machine learned advice. In Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pages 1834-1845. SIAM, 2020.
[28] Thodoris Lykouris and Sergei Vassilvitskii. Competitive caching with machine learned advice. Journal of the ACM (JACM), 68(4):1-25, 2021.
[29] Sungjin Im, Ravi Kumar, Aditya Petety, and Manish Purohit. Parsimonious learning-augmented caching. In International Conference on Machine Learning, pages 9588-9601. PMLR, 2022.
[30] Antonios Antoniadis, Christian Coester, Marek Elias, Adam Polak, and Bertrand Simon. Online metric algorithms with untrusted predictions. In International Conference on Machine Learning, pages 345-355. PMLR, 2020.
[31] Etienne Bamas, Andreas Maggiori, and Ola Svensson. The primal-dual method for learning augmented algorithms. Advances in Neural Information Processing Systems, 33:20083-20094, 2020.
[32] Keerti Anand, Rong Ge, Amit Kumar, and Debmalya Panigrahi. Online algorithms with multiple predictions. In International Conference on Machine Learning, pages 582-598. PMLR, 2022.
[33] Tongxin Li, Ruixiao Yang, Guannan Qu, Yiheng Lin, Adam Wierman, and Steven H Low. Certifying black-box policies with stability for nonlinear control. IEEE Open Journal of Control Systems, 2023.
[34] Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Ali Vakilian, and Nikos Zarifis. Learning online algorithms with distributional advice. In International Conference on Machine Learning, pages 2687-2696. PMLR, 2021.
[35] Yiheng Lin, Yang Hu, Guanya Shi, Haoyuan Sun, Guannan Qu, and Adam Wierman. Perturbation-based regret analysis of predictive control in linear time varying systems. Advances in Neural Information Processing Systems, 34:5174-5185, 2021.
[36] Yiheng Lin, Yang Hu, Guannan Qu, Tongxin Li, and Adam Wierman. Bounded-regret mpc via perturbation analysis: Prediction error, constraints, and nonlinearity. In Advances in Neural Information Processing Systems, 2022.
[37] David Hoeller, Farbod Farshidian, and Marco Hutter. Deep value model predictive control. In Conference on Robot Learning, pages 990-1004. PMLR, 2020.
[38] Richard Cheng, Abhinav Verma, Gabor Orosz, Swarat Chaudhuri, Yisong Yue, and Joel Burdick. Control regularization for reduced variance reinforcement learning. In International Conference on Machine Learning, pages 1141-1150. PMLR, 2019.
[39] Lukas Brunke, Melissa Greeff, Adam W Hall, Zhaocong Yuan, Siqi Zhou, Jacopo Panerati, and Angela P Schoellig. Safe learning in robotics: From learning-based control to safe reinforcement learning. Annual Review of Control, Robotics, and Autonomous Systems, 5:411-444, 2022.
[40] Lin Guan, Mudit Verma, Suna Sihang Guo, Ruohan Zhang, and Subbarao Kambhampati. Widening the pipeline in human-guided reinforcement learning with explanation and context-aware data augmentation. Advances in Neural Information Processing Systems, 34:21885-21897, 2021.
[41] Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. Advances in neural information processing systems, 30, 2017.
[42] James MacGlashan, Mark K Ho, Robert Loftin, Bei Peng, Guan Wang, David L Roberts, Matthew E Taylor, and Michael L Littman. Interactive learning from policy-dependent human feedback. In International Conference on Machine Learning, pages 2285-2294. PMLR, 2017.