1. Alacaoglu, A., Malitsky,Y.: Stochastic variance reduction for variational inequality methods. In: Conference on Learning Theory, 778–816. PMLR, (2022)
2. Bach, F., Levy, K.Y.: A universal algorithm for variational inequalities adaptive to smoothness and noise. In: Conference on learning theory, 164–194. PMLR, (2019)
3. Beznosikov, A., Sadiev, A., Gasnikov, A.: Gradient-free methods with inexact oracle for convex-concave stochastic saddle-point problem. In: International Conference on Mathematical Optimization Theory and Operations Research, 105–119. Springer, (2020)
4. Chavdarova,T., Gidel, G., Fleuret, F., Lacoste-Julien, S.: Reducing noise in gan training with variance reduced extragradient. Adv. Neural Inf. Proc. Syst. 32, (2019)
5. Chen, Y., Li, L., Wang, M.: Scalable bilinear pi learning using state and action features. In: International Conference on Machine Learning, 834–843. PMLR, (2018)