Local AdaGrad-type algorithm for stochastic convex-concave optimization

Author:

Liao Luofeng,Shen LiORCID,Duan Jia,Kolar Mladen,Tao Dacheng

Funder

Science and Technology Innovation 2030

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference59 articles.

1. Antonakopoulos, K., Belmega, V., & Mertikopoulos, P. (2021). Adaptive extra-gradient methods for min-max optimization and games. In International conference on learning representations.

2. Arjovsky, M. & Bottou, L. (2017). Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862.

3. Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein generative adversarial networks. In D. Precup & Y. W. Teh, (Eds.) Proceedings of the 34th international conference on machine learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, volume 70 of Proceedings of Machine Learning Research, (pp. 214–223). PMLR.

4. Azizian, W., Mitliagkas, I., Lacoste-Julien, S., & Gidel, G. (2020). A tight and unified analysis of gradient-based methods for a whole spectrum of differentiable games. In International conference on artificial intelligence and statistics, (pp. 2863–2873). PMLR.

5. Babanezhad, R. & Lacoste-Julien, S. (2020). Geometry-aware universal mirror-prox. arXiv preprint arXiv:2011.11203.

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