[31] Martin Jaggi. Revisiting Frank-Wolfe: Projection-free sparse convex optimization. In International Conference on Machine Learning, pages 427-435, 2013.
[32] Aren Karapetyan, Andrea Iannelli, and John Lygeros. On the regret of  $\mathcal{H}_{\infty}$  control. In IEEE Conference on Decision and Control, pages 6181-6186, 2022.
[33] Kihyun Kim and Insoon Yang. Distributional robustness in minimax linear quadratic control with Wasserstein distance. SIAM Journal on Control and Optimization, 61(2):458-483, 2023.
[34] Georgios Kotsalis, Guanghui Lan, and Arkadi S. Nemirovski. Convex optimization for finite-horizon robust covariance control of linear stochastic systems. SIAM Journal on Control and Optimization, 59(1):296-319, 2021.
[35] Evgenii S. Levitin and Boris T. Polyak. Constrained minimization methods. USSR Computational Mathematics and Mathematical Physics, 6(5):1-50, 1966.
[36] Peyman Mohajerin Esfahani and Daniel Kuhn. Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations. *Mathematical Programming*, 171(1-2):115-166, 2018.
[37] MOSEK ApS. The MOSEK Optimization Toolbox. Version 9.2., 2019.
[38] Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Daniel Kuhn, and Peyman Mohajerin Esfahani. Bridging Bayesian and minimax mean square error estimation via Wasserstein distributionally robust optimization. Mathematics of Operations Research, 48(1):1-37, 2023.
[39] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in PyTorch. In NIPS 2017 Autodiff Workshop, 2017.
[40] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems, pages 8026-8037, 2019.
[41] Ian R. Petersen, Matthew R. James, and Paul Dupuis. Minimax optimal control of stochastic uncertain systems with relative entropy constraints. IEEE Transactions on Automatic Control, 45(3):398-412, 2000.
[42] Gabriel Peyré and Marco Cuturi. Computational optimal transport: With applications to data science. Foundations and Trends in Machine Learning, 11(5-6):355-607, 2019.
[43] R. Tyrrell Rockafellar. Conjugate Duality and Optimization. SIAM, 1974.
[44] Soroosh Shafieezadeh-Abadeh, Viet Anh Nguyen, Daniel Kuhn, and Peyman Mohajerin Esfahani. Wasserstein distributionally robust Kalman filtering. In Advances in Neural Information Processing Systems, pages 8483-8492, 2018.
[45] Maurice Sion. On general minimax theorems. Pacific Journal of Mathematics, 8(1):171-176, 1958.
[46] Joelle Skaf and Stephen P. Boyd. Design of affine controllers via convex optimization. IEEE Transactions on Automatic Control, 55(11):2476-2487, 2010.
[47] Emanuel Todorov and Michael I. Jordan. Optimal feedback control as a theory of motor coordination. Nature Neuroscience, 5(11):1226-1235, 2002.
[48] James Townsend, Niklas Koep, and Sebastian Weichwald. Pymanopt: A Python toolbox for optimization on manifolds using automatic differentiation. Journal of Machine Learning Research, 17(137):1-5, 2016.