[19] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale GAN training for high fidelity natural image synthesis. In International Conference on Learning Representations, 2019.
[20] Sébastien Bubeck. Convex optimization: Algorithms and complexity. Foundations and Trends® in Machine Learning, 8(3-4):231–357, 2015.
[21] Mark Bun, Marco Gaboardi, Max Hopkins, Russell Impagliazzo, Rex Lei, Toniann Pitassi, Satchit Sivakumar, and Jessica Sorrell. Stability is stable: Connections between replicability, privacy, and adaptive generalization. In Proceedings of the 55th Annual ACM Symposium on Theory of Computing, pages 520-527, 2023.
[22] Tatjana Chavdarova, Matteo Pagliardini, Sebastian U Stich, François Fleuret, and Martin Jaggi. Taming GANs with lookahead-minmax. In International Conference on Learning Representations, 2021.
[23] Tong Che, Yanran Li, Athul Jacob, Yoshua Bengio, and Wenjie Li. Mode regularized generative adversarial networks. In International Conference on Learning Representations, 2017.
[24] Yuansi Chen, Chi Jin, and Bin Yu. Stability and convergence trade-off of iterative optimization algorithms. arXiv preprint arXiv:1804.01619, 2018.
[25] Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, and Le Song. SBEED: Convergent reinforcement learning with nonlinear function approximation. In International Conference on Machine Learning, pages 1125-1134. PMLR, 2018.
[26] Alexandre d'Aspremont. Smooth optimization with approximate gradient. SIAM Journal on Optimization, 19(3):1171-1183, 2008.
[27] Olivier Devolder, François Glineur, Yurii Nesterov, et al. First-order methods with inexact oracle: the strongly-convex case. CORE Discussion Papers, 2013016:47, 2013.
[28] Olivier Devolder, François Glineur, and Yurii Nesterov. First-order methods of smooth convex optimization with inexact oracle. Mathematical Programming, 146:37-75, 2014.
[29] Cynthia Dwork and Aaron Roth. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3-4):211-407, 2014.
[30] Hossein Esfandiari, Alkis Kalavasis, Amin Karbasi, Andreas Krause, Vahab Mirrokni, and Grigoris Velegkas. Replicable bandits. In International Conference on Learning Representations, 2023.
[31] Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, and Felix Zhou. Replicable clustering. arXiv preprint arXiv:2302.10359, 2023.
[32] Francisco Facchinei and Jong-Shi Pang. Finite-dimensional variational inequalities and complementarity problems. Springer, 2003.
[33] Farzan Farnia and Asuman Ozdaglar. Train simultaneously, generalize better: Stability of gradient-based minimax learners. In International Conference on Machine Learning, pages 3174-3185. PMLR, 2021.
[34] Vitaly Feldman, Tomer Koren, and Kunal Talwar. Private stochastic convex optimization: optimal rates in linear time. In Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, pages 439-449, 2020.
[35] Gauthier Gidel, Hugo Berard, Gaetan Vignoud, Pascal Vincent, and Simon Lacoste-Julien. A variational inequality perspective on generative adversarial networks. In International Conference on Learning Representations, 2019.
[36] Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Rémi Le Priol, Gabriel Huang, Simon Lacoste-Julien, and Ioannis Mitliagkas. Negative momentum for improved game dynamics. In International Conference on Artificial Intelligence and Statistics, pages 1802-1811. PMLR, 2019.