Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems
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[1] Shuzhong Zhang,et al. On lower iteration complexity bounds for the convex concave saddle point problems , 2019, Math. Program..
[2] Ya-Ping Hsieh,et al. The limits of min-max optimization algorithms: convergence to spurious non-critical sets , 2020, ICML.
[3] Meisam Razaviyayn,et al. Efficient Search of First-Order Nash Equilibria in Nonconvex-Concave Smooth Min-Max Problems , 2020, SIAM J. Optim..
[4] Jacob Abernethy,et al. Last-iterate convergence rates for min-max optimization , 2019, ArXiv.
[5] Tanner Fiez,et al. Gradient Descent-Ascent Provably Converges to Strict Local Minmax Equilibria with a Finite Timescale Separation , 2020, ArXiv.
[6] Alternating proximal-gradient steps for (stochastic) nonconvex-concave minimax problems , 2020, 2007.13605.
[7] Tanner Fiez,et al. Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study , 2020, ICML.
[8] H. Vincent Poor,et al. Enhanced First and Zeroth Order Variance Reduced Algorithms for Min-Max Optimization , 2020, ArXiv.
[9] Tianbao Yang,et al. Fast Objective and Duality Gap Convergence for Non-convex Strongly-concave Min-max Problems , 2020, ArXiv.
[10] Guanghui Lan,et al. A Unified Single-loop Alternating Gradient Projection Algorithm for Nonconvex-Concave and Convex-Nonconcave Minimax Problems , 2020, Mathematical programming.
[11] Babak Barazandeh,et al. Solving Non-Convex Non-Differentiable Min-Max Games Using Proximal Gradient Method , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[12] Michael I. Jordan,et al. Near-Optimal Algorithms for Minimax Optimization , 2020, COLT.
[13] Haishan Ye,et al. Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems , 2020, NeurIPS.
[14] Michael I. Jordan,et al. On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems , 2019, ICML.
[15] Michael I. Jordan,et al. What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? , 2019, ICML.
[16] S. Shankar Sastry,et al. On Gradient-Based Learning in Continuous Games , 2018, SIAM J. Math. Data Sci..
[17] Yujun Li,et al. A Stochastic Proximal Point Algorithm for Saddle-Point Problems , 2019, ArXiv.
[18] Georgios Piliouras,et al. Finite Regret and Cycles with Fixed Step-Size via Alternating Gradient Descent-Ascent , 2019, COLT.
[19] Prateek Jain,et al. Efficient Algorithms for Smooth Minimax Optimization , 2019, NeurIPS.
[20] Lillian J. Ratliff,et al. Convergence of Learning Dynamics in Stackelberg Games , 2019, ArXiv.
[21] Tamer Basar,et al. Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games , 2019, NeurIPS.
[22] Tatjana Chavdarova,et al. Reducing Noise in GAN Training with Variance Reduced Extragradient , 2019, NeurIPS.
[23] Jason D. Lee,et al. Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods , 2019, NeurIPS.
[24] Yongxin Chen,et al. On the Global Convergence of Imitation Learning: A Case for Linear Quadratic Regulator , 2019, ArXiv.
[25] Liwei Wang,et al. Gradient Descent Finds Global Minima of Deep Neural Networks , 2018, ICML.
[26] Ioannis Mitliagkas,et al. Negative Momentum for Improved Game Dynamics , 2018, AISTATS.
[27] Rong Jin,et al. Robust Optimization over Multiple Domains , 2018, AAAI.
[28] Wei Hu,et al. Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong Convexity , 2018, AISTATS.
[29] Y. Nesterov,et al. Linear convergence of first order methods for non-strongly convex optimization , 2015, Math. Program..
[30] Mingrui Liu,et al. Solving Weakly-Convex-Weakly-Concave Saddle-Point Problems as Successive Strongly Monotone Variational Inequalities , 2018 .
[31] Constantinos Daskalakis,et al. The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization , 2018, NeurIPS.
[32] Sham M. Kakade,et al. Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator , 2018, ICML.
[33] Sebastian Nowozin,et al. Which Training Methods for GANs do actually Converge? , 2018, ICML.
[34] Le Song,et al. SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation , 2017, ICML.
[35] John C. Duchi,et al. Certifying Some Distributional Robustness with Principled Adversarial Training , 2017, ICLR.
[36] Constantinos Daskalakis,et al. Training GANs with Optimism , 2017, ICLR.
[37] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[38] Robert S. Chen,et al. Robust Optimization for Non-Convex Objectives , 2017, NIPS.
[39] David Pfau,et al. Unrolled Generative Adversarial Networks , 2016, ICLR.
[40] John C. Duchi,et al. Variance-based Regularization with Convex Objectives , 2016, NIPS.
[41] Le Song,et al. Learning from Conditional Distributions via Dual Embeddings , 2016, AISTATS.
[42] Mengdi Wang,et al. Stochastic Primal-Dual Methods and Sample Complexity of Reinforcement Learning , 2016, ArXiv.
[43] Alexander J. Smola,et al. Fast incremental method for smooth nonconvex optimization , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).
[44] Yi Zhou,et al. Geometrical properties and accelerated gradient solvers of non-convex phase retrieval , 2016, 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[45] Mark W. Schmidt,et al. Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition , 2016, ECML/PKDD.
[46] Ming-Yu Liu,et al. Coupled Generative Adversarial Networks , 2016, NIPS.
[47] Francis R. Bach,et al. Stochastic Variance Reduction Methods for Saddle-Point Problems , 2016, NIPS.
[48] Alexander J. Smola,et al. Stochastic Variance Reduction for Nonconvex Optimization , 2016, ICML.
[49] John Wright,et al. A Geometric Analysis of Phase Retrieval , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).
[50] John C. Duchi,et al. Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences , 2016, NIPS.
[51] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[52] Lin Xiao,et al. A Proximal Stochastic Gradient Method with Progressive Variance Reduction , 2014, SIAM J. Optim..
[53] Roberto Todeschini,et al. Prediction of Acute Aquatic Toxicity toward Daphnia Magna by using the GA-kNN Method , 2014, Alternatives to laboratory animals : ATLA.
[54] Tong Zhang,et al. Accelerating Stochastic Gradient Descent using Predictive Variance Reduction , 2013, NIPS.
[55] Hui Zhang,et al. Gradient methods for convex minimization: better rates under weaker conditions , 2013, ArXiv.
[56] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[57] Yurii Nesterov,et al. Solving Strongly Monotone Variational and Quasi-Variational Inequalities , 2006 .
[58] F. Facchinei,et al. Finite-Dimensional Variational Inequalities and Complementarity Problems , 2003 .
[59] Laurent El Ghaoui,et al. Robust Solutions to Least-Squares Problems with Uncertain Data , 1997, SIAM J. Matrix Anal. Appl..
[60] Z.-Q. Luo,et al. Error bounds and convergence analysis of feasible descent methods: a general approach , 1993, Ann. Oper. Res..
[61] G. M. Korpelevich. The extragradient method for finding saddle points and other problems , 1976 .
[62] M. Bacharach. Two-person Cooperative Games , 1976 .
[63] J. Neumann,et al. Theory of games and economic behavior , 1945, 100 Years of Math Milestones.