[39] Eduard Gorbunov, Marina Danilova, Innokentiy Shibaev, Pavel Dvurechensky, and Alexander Gasnikov. Near-optimal high probability complexity bounds for non-smooth stochastic optimization with heavy-tailed noise. arXiv preprint arXiv:2106.05958, 2021.
[40] Yuze Han, Guangzeng Xie, and Zhihua Zhang. Lower complexity bounds of finite-sum optimization problems: The results and construction. arXiv preprint arXiv:2103.08280, 2021.
[41] P. T. Harker and J.-S. Pang. Finite-dimensional variational inequality and nonlinear complementarity problems: a survey of theory, algorithms and applications. Mathematical programming, 1990.
[42] Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, and Panayotis Mertikopoulos. On the convergence of single-call stochastic extra-gradient methods. Advances in Neural Information Processing Systems, 32, 2019.
[43] Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, and Panayotis Mertikopoulos. Explore aggressively, update conservatively: Stochastic extragradient methods with variable stepsize scaling. Advances in Neural Information Processing Systems, 33:16223-16234, 2020.
[44] Chonghai Hu, Weike Pan, and James Kwok. Accelerated gradient methods for stochastic optimization and online learning. Advances in Neural Information Processing Systems, 22, 2009.
[45] A. N. Iusem, A. Jofre, R. I. Oliveira, and P. Thompson. Extragradient method with variance reduction for stochastic variational inequalities. SIAM Journal on Optimization, 27(2):686-724, 2017.
[46] Alfredo N Iusem, Alejandro Jofré, and Philip Thompson. Incremental constraint projection methods for monotone stochastic variational inequalities. Mathematics of Operations Research, 44(1):236-263, 2019.
[47] Prateek Jain, Sham M Kakade, Rahul Kidambi, Praneeth Netrapalli, and Aaron Sidford. Accelerating stochastic gradient descent for least squares regression. In Conference On Learning Theory, pages 545-604. PMLR, 2018.
[48] Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, and Aaron Sidford. Parallelizing stochastic gradient descent for least squares regression: Mini-batching, averaging, and model misspecification. Journal of Machine Learning Research, 18(223):1-42, 2018.
[49] Houyuan Jiang and Huifu Xu. Stochastic approximation approaches to the stochastic variational inequality problem. IEEE Transactions on Automatic Control, 53(6):1462-1475, 2008.
[50] Yujia Jin and Aaron Sidford. Efficiently solving MDPs with stochastic mirror descent. In Proceedings of the 37th International Conference on Machine Learning (ICML), volume 119, pages 4890-4900. PMLR, 2020.
[51] Thorsten Joachims. A support vector method for multivariate performance measures. pages 377-384, 01 2005.
[52] Anatoli Juditsky, Arkadi Nemirovski, and Claire Tauvel. Solving variational inequalities with stochastic mirror-prox algorithm. Stochastic Systems, 1(1):17-58, 2011.
[53] Aswin Kannan and Uday V Shanbhag. Optimal stochastic extragradient schemes for pseudomonotone stochastic variational inequality problems and their variants. Computational Optimization and Applications, 74(3):779-820, 2019.
[54] Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, and Sham Kakade. On the insufficiency of existing momentum schemes for stochastic optimization. In 2018 Information Theory and Applications Workshop (ITA), pages 1-9. IEEE, 2018.
[55] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.