Fetching the paper…
Reading the bibliography…
This paper considers minimax optimization $\min_x \max_y f(x, y)$ in the challenging setting where $f$ can be both nonconvex in $x$ and nonconcave in $y$.
Zur theorie der gesellschaftsspiele
J. v. Neumann · 1928
Earlier work this paper cites.
An iterative method of solving a game
J. Robinson · 1951
Earlier work this paper cites.
Theory of ordinary differential equations
E. A. Coddington and N. Levinson · 1955
Earlier work this paper cites.
The extragradient method for finding saddle points and other problems
G. M. Korpelevich · 1976
Earlier work this paper cites.
Efficient algorithms for learning to play repeated games against computationally bounded adversaries
Y. Freund, M. Kearns, Y. Mansour, D. Ron, R. Rubinfeld, and R. E. Schapire · 1995
Earlier work this paper cites.
Coalitions among computationally bounded agents
T. W. Sandhlom and V. R. Lesser · 1997
Earlier work this paper cites.
Dynamic noncooperative game theory
T. Başar and G. J. Olsder · 1998
Earlier work this paper cites.
Ordinary Differential Equations
P. Hartman · 2002
Earlier work this paper cites.
Optimality and stability in non-convex-non-concave min-max optimization
G. Zhang, P. Poupart, and Y. Yu · 2002
Earlier work this paper cites.
Prox-method with rate of convergence o (1/t) for variational inequalities with lipschitz continuous monotone operators and smooth convex-concave saddle point problems
A. Nemirovski · 2004
Earlier work this paper cites.
Newton-type methods for minimax optimization
G. Zhang, K. Wu, P. Poupart, and Y. Yu · 2006
Earlier work this paper cites.
Convex optimization theory
D. P. Bertsekas · 2009
Earlier work this paper cites.
On the duality of strong convexity and strong smoothness: Learning applications and matrix regularization
S. Kakade, S. Shalev-Shwartz, A. Tewari, et al · 2009
Earlier work this paper cites.
Multi-agent learning with policy prediction
C. Zhang and V. Lesser · 2010
Earlier work this paper cites.
Numerical solution of ordinary differential equations , volume 108
K. Atkinson, W. Han, and D. E. Stewart · 2011
Earlier work this paper cites.
Evasion attacks against machine learning at test time
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. 𝐯 \mathbf{v} Srndić, P. Laskov, G. Giacinto, and F. Roli · 2013
Earlier work this paper cites.
Characterization and computation of local Nash equilibria in continuous games
L. J. Ratliff, S. A. Burden, and S. S. Sastry · 2013
Earlier work this paper cites.
Generative adversarial networks
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Decision theory with resource-bounded agents
J. Y. Halpern, R. Pass, and L. Seeman · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
Earlier work this paper cites.
A faster cutting plane method and its implications for combinatorial and convex optimization
Y. T. Lee, A. Sidford, and S. C.-w. Wong · 2015
Cited alongside, same era.
On the characterization of local Nash equilibria in continuous games
L. J. Ratliff, S. A. Burden, and S. S. Sastry · 2016
Cited alongside, same era.
Adversarial machine learning at scale
A. Kurakin, I. J. Goodfellow, and S. Bengio · 2017
Cited alongside, same era.
The numerics of gans
L. Mescheder, S. Nowozin, and A. Geiger · 2017
Cited alongside, same era.
Unrolled generative adversarial networks
L. Metz, B. Poole, D. Pfau, and J. Sohl-Dickstein · 2017
Cited alongside, same era.
Gradient descent gan optimization is locally stable
V. Nagarajan and J. Z. Kolter · 2017
Cited alongside, same era.
Finite regret and cycles with fixed step-size via alternating gradient descent-ascent
J. P. Bailey, G. Gidel, and G. Piliouras · 2020
Later among the works it cites.
Opponent anticipation via conjectural variations
B. Chasnov, T. Fiez, and L. J. Ratliff · 2020
Later among the works it cites.
Do gans always have nash equilibria?
F. Farnia and A. Ozdaglar · 2020
Later among the works it cites.
Implicit learning dynamics in stackelberg games: Equilibria characterization, convergence analysis, and empirical study
T. Fiez, B. Chasnov, and L. Ratliff · 2020
Later among the works it cites.
The limits of min-max optimization algorithms: Convergence to spurious non-critical sets
Y.-P. Hsieh, P. Mertikopoulos, and V. Cevher · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The mechanics of n-player differentiable games
D. Balduzzi, S. Racaniere, J. Martens, J. Foerster, K. Tuyls, and T. Graepel · 2018
Cited alongside, same era.
D. Davis and D. Drusvyatskiy · 2018
Cited alongside, same era.
Learning with opponent-learning awareness
J. Foerster, R. Y. Chen, M. Al-Shedivat, S. Whiteson, P. Abbeel, and I. Mordatch · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
Cited alongside, same era.
Which training methods for gans do actually converge?
L. Mescheder, A. Geiger, and S. Nowozin · 2018
Cited alongside, same era.
Local saddle point optimization: A curvature exploitation approach
L. Adolphs, H. Daneshmand, A. Lucchi, and T. Hofmann · 2019
Cited alongside, same era.
C. Jin, P. Netrapalli, and M. Jordan · 2020
Later among the works it cites.
Gans with first-order greedy discriminators
V. Keswani, O. Mangoubi, S. Sachdeva, and N. K. Vishnoi · 2020
Later among the works it cites.
Hybrid block successive approximation for one-sided non-convex min-max problems: algorithms and applications
S. Lu, I. Tsaknakis, M. Hong, and Y. Chen · 2020
Later among the works it cites.
Stochastic recursive gradient descent ascent for stochastic nonconvex-strongly-concave minimax problems
L. Luo, H. Ye, Z. Huang, and T. Zhang · 2020
Later among the works it cites.
On gradient-based learning in continuous games
E. Mazumdar, L. J. Ratliff, and S. S. Sastry · 2020
Later among the works it cites.
Efficient search of first-order nash equilibria in nonconvex-concave smooth min-max problems
D. M. Ostrovskii, A. Lowy, and M. Razaviyayn · 2020
Later among the works it cites.
On solving minimax optimization locally: A follow-the-ridge approach
Y. Wang, G. Zhang, and J. Ba · 2020
Later among the works it cites.
A primal dual smoothing framework for max-structured nonconvex optimization
R. Zhao · 2020
Later among the works it cites.
The complexity of constrained min-max optimization
C. Daskalakis, S. Skoulakis, and M. Zampetakis · 2021
Closest in time.
Local convergence analysis of gradient descent ascent with finite timescale separation
T. Fiez and L. Ratliff · 2021
Closest in time.
On the impossibility of global convergence in multi-loss optimization
A. Letcher · 2021
Closest in time.
Complexity lower bounds for nonconvex-strongly-concave min-max optimization
H. Li, Y. Tian, J. Zhang, and A. Jadbabaie · 2021
Closest in time.
Greedy adversarial equilibrium: An efficient alternative to nonconvex-nonconcave min-max optimization
O. Mangoubi and N. K. Vishnoi · 2021
Closest in time.
Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning
H. Rafique, M. Liu, Q. Lin, and T. Yang · 2021
Closest in time.
The complexity of nonconvex-strongly-concave minimax optimization
S. Zhang, J. Yang, C. Guzmán, N. Kiyavash, and N. He · 2021
Closest in time.