A. D. Flaxman, A. T. Kalai, and H. B. McMahan. Online convex optimization in the bandit setting: Gradient descent without a gradient. In Proceedings of the 16th Annual ACM-SIAM Symposium on Discrete Algorithms, pages 385-394, 2005.
Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139, 1997.
Y. Freund and R. E. Schapire. Adaptive game playing using multiplicative weights. Games and Economic Behavior, 29(1):79-103, 1999.
N. Haghtalab, M. I. Jordan, and E. Zhao. On-demand sampling: Learning optimally from multiple distributions. In Advances in Neural Information Processing Systems 35, pages 406-419, 2022.
N. Haghtalab, M. I. Jordan, and E. Zhao. On-demand sampling: Learning optimally from multiple distributions. ArXiv e-prints, arXiv:2210.12529v2, 2023.
T. Hashimoto, M. Srivastava, H. Namkoong, and P. Liang. Fairness without demographics in repeated loss minimization. In Proceedings of the 35th International Conference on Machine Learning, pages 1929-1938, 2018.
W. Hu, G. Niu, I. Sato, and M. Sugiyama. Does distributionally robust supervised learning give robust classifiers? In Proceedings of the 35th International Conference on Machine Learning, volume 80, pages 2029-2037, 2018.
J. Jin, B. Zhang, H. Wang, and L. Wang. Non-convex distributionally robust optimization: Non-asymptotic analysis. In Advances in Neural Information Processing Systems 34, pages 2771-2782, 2021.
A. Juditsky, A. Nemirovski, and C. Tauvel. Solving variational inequalities with stochastic mirror-prox algorithm. Stochastic Systems, 1(1):17-58, 2011.
A. B. Juditsky and A. S. Nemirovski. Large deviations of vector-valued martingales in 2-smooth normed spaces. ArXiv e-prints, arXiv:0809.0813, 2008.
T. Kocák, G. Neu, M. Valko, and R. Munos. Efficient learning by implicit exploration in bandit problems with side observations. In Advances in Neural Information Processing Systems 27, pages 613-621, 2014.
D. Kuhn, P. M. Esfahani, V. A. Nguyen, and S. Shafieezadeh-Abadeh. Wasserstein distributionally robust optimization: Theory and applications in machine learning. Operations Research & Management Science in the Age of Analytics, pages 130–166, 2019.
H. J. Kushner and G. G. Yin. Stochastic Approximation and Recursive Algorithms and Applications. Springer, second edition, 2003.
G. Lan. An optimal method for stochastic composite optimization. Mathematical Programming, 133: 365-397, 2012.
T. Lattimore and C. Szepesvári. Bandit Algorithms. Cambridge University Press, 2020.
D. Levy, Y. Carmon, J. C. Duchi, and A. Sidford. Large-scale methods for distributionally robust optimization. In Advances in Neural Information Processing Systems 33, pages 8847-8860, 2020.
M. Mohri, G. Sivek, and A. T. Suresh. Agnostic federated learning. In Proceedings of the 36th International Conference on Machine Learning, pages 4615-4625, 2019.
H. Namkoong and J. C. Duchi. Stochastic gradient methods for distributionally robust optimization with  $f$ -divergences. In Advances in Neural Information Processing Systems 29, pages 2216-2224, 2016.
H. Namkoong and J. C. Duchi. Variance-based regularization with convex objectives. In Advances in Neural Information Processing Systems 30, pages 2971-2980, 2017.