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Generative adversarial networks (GANs) have enjoyed tremendous success in image generation and processing, and have recently attracted growing interests in financial modelings.
On general minimax theorems
Maurice Sion · 1958
Earlier work this paper cites.
On the theory of games of strategy
John Von Neumann · 1959
Earlier work this paper cites.
Partial Differential Equations
Lawrence C Evans · 1998
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A computational fluid mechanics solution to the monge-kantorovich mass transfer problem
Jean-David Benamou and Yann Brenier · 2000
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Diffusions, Markov Processes and Martingales Volume 2: Itô Calculus
L. Chris G. Rogers and David Williams · 2000
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Generative visual manipulation on the natural image manifold
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A Efros · 2002
Earlier work this paper cites.
Mean field games
Jean-Michel Lasry and Pierre-Louis Lions · 2007
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Optimal Transport: Old and New
Cédric Villani · 2008
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Mean field games: numerical methods
Yves Achdou and Italo Capuzzo-Dolcetta · 2010
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Mean Field Games and Mean Field Type Control Theory
Alain Bensoussan, Jens Frehse, and Phillip Yam · 2013
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A fully discrete semi-Lagrangian scheme for a first order mean field game problem
Elisabetta Carlini and Francisco J. Silva · 2014
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Martingale optimal transport and robust hedging in continuous time
Yan Dolinsky and H Mete Soner · 2014
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Generative adversarial nets
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Deep generative image models using a Laplacian pyramid of adversarial networks
Emily L Denton, Soumith Chintala, Arthur Szlam, and Rob Fergus · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Guided cost learning: deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and Pieter Abbeel · 2016
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Contextual RNN-GANs for abstract reasoning diagram generation
Arnab Ghosh, Viveka Kulharia, Amitabha Mukerjee, Vinay Namboodiri, and Mohit Bansal · 2016
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, and Zehan Wang · 2016
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Multi-martingale optimal transport
Tongseok Lim · 2016
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Semantic segmentation using adversarial networks
Pauline Luc, Camille Couprie, Soumith Chintala, and Jakob Verbeek · 2016
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Generative adversarial text to image synthesis
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee · 2016
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Generating videos with scene dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
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Semantic image inpainting with perceptual and contextual losses
Raymond Yeh, Chen Chen, Teck Yian Lim, Mark Hasegawa-Johnson, and Minh N Do · 2016
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Two numerical approaches to stationary mean-field games
Noha Almulla, Rita Ferreira, and Diogo Gomes · 2017
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Complete duality for martingale optimal transport on the line
Mathias Beiglböck, Marcel Nutz, and Nizar Touzi · 2017
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Improved training of Wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Adversarial uncertainty quantification in physics-informed neural networks
Yibo Yang and Paris Perdikaris · 2018
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Convergence analysis of machine learning algorithms for the numerical solution of mean field control and games: I - the ergodic case
René Carmona and Mathieu Laurière · 2019
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Convergence analysis of machine learning algorithms for the numerical solution of mean field control and games: II - the finite horizon case
René Carmona and Mathieu Laurière · 2019
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Deep learning in asset pricing
Luyang Chen, Markus Pelger, and Jason Zhu · 2019
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Smoothness and stability in GANs
Casey Chu, Kentaro Minami, and Kenji Fukumizu · 2019
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Tightness and duality of martingale transport on the Skorokhod space
Gaoyue Guo, Xiaolu Tan, and Nizar Touzi · 2017
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Relaxed Wasserstein with applications to GANs
Xin Guo, Johnny Hong, Tianyi Lin, and Nan Yang · 2017
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f-GANs in an information geometric nutshell
Richard Nock, Zac Cranko, Aditya K Menon, Lizhen Qu, and Robert C Williamson · 2017
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Solving nonlinear and high-dimensional partial differential equations via deep learning
Ali Al-Aradi, Adolfo Correia, Danilo Naiff, Gabriel Jardim, and Yuri Saporito · 2018
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Probabilistic Theory of Mean Field Games with Applications I: Mean Field FBSDEs, Control, and Games
René Carmona and François Delarue · 2018
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Probabilistic Theory of Mean Field Games with Applications II: Mean Field Games with Common Noise and Master Equations
René Carmona and François Delarue · 2018
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Computational methods for martingale optimal transport problems
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Learning mean-field games
Xin Guo, Anran Hu, Renyuan Xu, and Junzi Zhang · 2019
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Stochastic games for fuel follower problem: N versus mean field game
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A geometric view of optimal transportation and generative model
Na Lei, Kehua Su, Li Cui, Shing-Tung Yau, and Xianfeng David Gu · 2019
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Multiperiod martingale transport
Marcel Nutz, Florian Stebegg, and Xiaowei Tan · 2019
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BreGMN: scaled-Bregman generative modeling networks
Akash Srivastava, Kristjan Greenewald, and Farzaneh Mirzazadeh · 2019
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Deep hedging: learning to simulate equity option markets
Magnus Wiese, Lianjun Bai, Ben Wood, J P Morgan, and Hans Buehler · 2019
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Stock market prediction based on generative adversarial network
Kang Zhang, Guoqiang Zhong, Junyu Dong, Shengke Wang, and Yong Wang · 2019
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A closer look at the optimization landscape of generative adversarial networks
Hugo Berard, Gauthier Gidel, Amjad Almahairi, Pascal Vincent, and Simon Lacoste-Julien · 2020
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Approximation and convergence of GANs training: an SDE approach
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Game on random environment, mean-field Langevin system and neural networks
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A mean-field analysis of two-player zero-sum games
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Generalization properties of optimal transport gans with latent distribution learning
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Quant GANs: deep generation of financial time series
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Deconstructing generative adversarial networks
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