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Generative Adversarial Networks (GANs) are a powerful class of generative models in the deep learning community.
The extragradient method for finding saddle points and other problems
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Prox-method with rate of convergence o (1/t) for variational inequalities with lipschitz continuous monotone operators and smooth convex-concave saddle point problems
Arkadi Nemirovski · 2004
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Yurii Nesterov · 2007
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S Sundhar Ram, A Nedić, and Venugopal V Veeravalli · 2009
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Arkadi Nemirovski, Anatoli Juditsky, Guanghui Lan, and Alexander Shapiro · 2009
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Distributed subgradient methods for multi-agent optimization
Angelia Nedic and Asuman Ozdaglar · 2009
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Bandwidth optimal all-reduce algorithms for clusters of workstations
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Kunal Srivastava, Angelia Nedic, and Duvsan Stipanovic · 2011
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Konstantinos I Tsianos, Sean Lawlor, and Michael G Rabbat · 2012
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Distributed alternating direction method of multipliers
Ermin Wei and Asuman Ozdaglar · 2012
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Chao-Kai Chiang, Tianbao Yang, Chia-Jung Lee, Mehrdad Mahdavi, Chi-Jen Lu, Rong Jin, and Shenghuo Zhu · 2012
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Sasha Rakhlin and Karthik Sridharan · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Fast distributed gradient methods
Duvsan Jakovetic, Joao Xavier, and JoseMF Moura · 2014
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Ruiliang Zhang and James T Kwok · 2014
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On the linear convergence of the admm in decentralized consensus optimization
Wei Shi, Qing Ling, Kun Yuan, Gang Wu, and Wotao Yin · 2014
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Distributed stochastic optimization and learning
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A saddle point algorithm for networked online convex optimization
Alec Koppel, Felicia Y Jakubiec, and Alejandro Ribeiro · 2015
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Distributed subgradient methods for saddle-point problems
David Mateos-Núnez and Jorge Cortés · 2015
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On the convergence properties of non-euclidean extragradient methods for variational inequalities with generalized monotone operators
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Multi-agent mirror descent for decentralized stochastic optimization
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On the convergence of decentralized gradient descent
Kun Yuan, Qing Ling, and Wotao Yin · 2016
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Consensus optimization with delayed and stochastic gradients on decentralized networks
Benjamin Sirb and Xiaojing Ye · 2016
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Dsa: Decentralized double stochastic averaging gradient algorithm
Aryan Mokhtari and Alejandro Ribeiro · 2016
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Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
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Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent
Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, and Ji Liu · 2017
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Extragradient method with variance reduction for stochastic variational inequalities
Non-convex min-max optimization: Provable algorithms and applications in machine learning
Hassan Rafique, Mingrui Liu, Qihang Lin, and Tianbao Yang · 2018
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The limit points of (optimistic) gradient descent in min-max optimization
Constantinos Daskalakis and Ioannis Panageas · 2018
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Accelerated decentralized optimization with local updates for smooth and strongly convex objectives
Hadrien Hendrikx, Laurent Massoulié, and Francis Bach · 2018
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Optimal algorithms for non-smooth distributed optimization in networks
Kevin Scaman, Francis Bach, Sébastien Bubeck, Laurent Massoulié, and Yin Tat Lee · 2018
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Communication-efficient algorithms for decentralized and stochastic optimization
Guanghui Lan, Soomin Lee, and Yi Zhou · 2018
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AN Iusem, Alejandro Jofré, Roberto I Oliveira, and Philip Thompson · 2017
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Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, and Haoyang Zeng · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Gradient descent gan optimization is locally stable
Vaishnavh Nagarajan and J Zico Kolter · 2017
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An online learning approach to generative adversarial networks
Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi, Thomas Hofmann, and Andreas Krause · 2017
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Stabilizing adversarial nets with prediction methods
Abhay Yadav, Sohil Shah, Zheng Xu, David Jacobs, and Tom Goldstein · 2017
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Asynchronous decentralized parallel stochastic gradient descent
Xiangru Lian, Wei Zhang, Ce Zhang, and Ji Liu · 2018
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D2: Decentralized training over decentralized data
Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, and Ji Liu · 2018
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Stochastic gradient push for distributed deep learning
Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, and Michael Rabbat · 2018
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Jianyu Wang and Gauri Joshi · 2018
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Variance-reduced stochastic learning by networked agents under random reshuffling
Kun Yuan, Bicheng Ying, Jiageng Liu, and Ali H Sayed · 2018
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An alternative view: When does sgd escape local minima?
Robert Kleinberg, Yuanzhi Li, and Yang Yuan · 2018
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A large-scale study on regularization and normalization in gans
Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, and Sylvain Gelly · 2018
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Reducing noise in gan training with variance reduced extragradient
Tatjana Chavdarova, Gauthier Gidel, Francois Fleuret, and Simon Lacoste-Julien · 2019
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