Fetching the paper…
Reading the bibliography…
Nonconvex-concave min-max problem arises in many machine learning applications including minimizing a pointwise maximum of a set of nonconvex functions and robust adversarial training of neural networks.
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
Earlier work this paper cites.
A. Forsgren, P. E. Gill, and M. H. Wright, “Interior methods for nonlinear optimization,” SIAM review
2002
Earlier work this paper cites.
A. Nemirovski, “Prox-method with rate of convergence o (1/t) for variational inequalities with lipschitz continuous monotone operators and smooth convex-concave saddle point problems,” SIAM Journal on Optimization
2004
Earlier work this paper cites.
Y. Nesterov, “Smooth minimization of non-smooth functions,” Mathematical programming
2005
Earlier work this paper cites.
Y. Nesterov, “Dual extrapolation and its applications to solving variational inequalities and related problems,” Mathematical Programming
2007
Earlier work this paper cites.
Springer Science & Business Media, 2007
F. Facchinei and J.-S. Pang, Finite-dimensional variational inequalities and complementarity problems · 2007
Earlier work this paper cites.
Princeton University Press, 2009
A. Ben-Tal, L. El Ghaoui, and A. Nemirovski, Robust optimization · 2009
Earlier work this paper cites.
P. Carbonetto, M. Schmidt, and N. D. Freitas, “An interior-point stochastic approximation method and an l1-regularized delta rule,” in Advances in neural information processing systems
2009
Earlier work this paper cites.
A. Krizhevsky, G. Hinton, et al
2009
Earlier work this paper cites.
E. Delage and Y. Ye, “Distributionally robust optimization under moment uncertainty with application to data-driven problems,” Operations research
2010
Earlier work this paper cites.
R. D. Monteiro and B. F. Svaiter, “On the complexity of the hybrid proximal extragradient method for the iterates and the ergodic mean,” SIAM Journal on Optimization
2010
Earlier work this paper cites.
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel, “Fairness through awareness,” in Proceedings of the 3rd innovations in theoretical computer science conference
2012
Earlier work this paper cites.
Springer Science & Business Media, 2012
M. Berger and B. Gostiaux, Differential Geometry: Manifolds, Curves, and Surfaces: Manifolds, Curves, and Surfaces · 2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
N. Parikh and S. Boyd, “Proximal algorithms,” Foundations and Trends in optimization
2014
Earlier work this paper cites.
J. Liang, J. Fadili, and G. Peyré, “Local linear convergence of forward–backward under partial smoothness,” in Advances in Neural Information Processing Systems
2014
Earlier work this paper cites.
Y. Xu and W. Yin, “Block stochastic gradient iteration for convex and nonconvex optimization,” SIAM Journal on Optimization
2015
Earlier work this paper cites.
H. Namkoong and J. C. Duchi, “Stochastic gradient methods for distributionally robust optimization with f-divergences,” in Advances in neural information processing systems
2016
Cited alongside, same era.
B. Palaniappan and F. Bach, “Stochastic variance reduction methods for saddle-point problems,” in Advances in Neural Information Processing Systems
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
M. Hardt, E. Price, and N. Srebro, “Equality of opportunity in supervised learning,” in Advances in neural information processing systems
2019
Later among the works it cites.
M. Nouiehed, M. Sanjabi, T. Huang, J. D. Lee, and M. Razaviyayn, “Solving a class of non-convex min-max games using iterative first order methods,” in Advances in Neural Information Processing Systems
2019
Later among the works it cites.
2019
Later among the works it cites.
K. K. Thekumparampil, P. Jain, P. Netrapalli, and S. Oh, “Efficient algorithms for smooth minimax optimization,” in Advances in Neural Information Processing Systems
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein gan,” arXiv preprint arXiv:1701.07875
2017
Cited alongside, same era.
2017
Cited alongside, same era.
H. Namkoong and J. C. Duchi, “Variance-based regularization with convex objectives,” in Advances in neural information processing systems
2017
Cited alongside, same era.
Y. Zhang and L. Xiao, “Stochastic primal-dual coordinate method for regularized empirical risk minimization,” The Journal of Machine Learning Research
2017
Cited alongside, same era.
S. S. Du, J. Chen, L. Li, L. Xiao, and D. Zhou, “Stochastic variance reduction methods for policy evaluation,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70
2017
Cited alongside, same era.
2017
Cited alongside, same era.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70
2017
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
M. Mohri, G. Sivek, and A. T. Suresh, “Agnostic federated learning,” arXiv preprint arXiv:1902.00146
2019
Later among the works it cites.
2019
Later among the works it cites.
L. Cannelli, F. Facchinei, V. Kungurtsev, and G. Scutari, “Asynchronous parallel algorithms for nonconvex optimization,” Mathematical Programming
2019
Later among the works it cites.
Y. Wang, W. Yin, and J. Zeng, “Global convergence of admm in nonconvex nonsmooth optimization,” Journal of Scientific Computing
2019
Later among the works it cites.
2019
Later among the works it cites.
2020
Closest in time.
2020
Closest in time.
T. Lin, C. Jin, M. Jordan, et al
2020
Closest in time.
2020
Closest in time.
J. Zhang and Z.-Q. Luo, “A proximal alternating direction method of multiplier for linearly constrained nonconvex minimization,” SIAM Journal on Optimization
2020
Closest in time.
2020
Closest in time.