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[1] Bernhard Schölkopf,et al. AdaGAN: Boosting Generative Models , 2017, NIPS.
[2] David Berthelot,et al. BEGAN: Boundary Equilibrium Generative Adversarial Networks , 2017, ArXiv.
[3] Santosh S. Vempala,et al. Efficient algorithms for online decision problems , 2005, J. Comput. Syst. Sci..
[4] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[5] Yoshua Bengio,et al. Mode Regularized Generative Adversarial Networks , 2016, ICLR.
[6] J. Nash. Equilibrium Points in N-Person Games. , 1950, Proceedings of the National Academy of Sciences of the United States of America.
[7] Ian J. Goodfellow,et al. NIPS 2016 Tutorial: Generative Adversarial Networks , 2016, ArXiv.
[8] David Pfau,et al. Unrolled Generative Adversarial Networks , 2016, ICLR.
[9] Sepp Hochreiter,et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.
[10] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[11] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[12] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[13] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[14] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[15] Y. Freund,et al. Adaptive game playing using multiplicative weights , 1999 .
[16] Sebastian Nowozin,et al. Stabilizing Training of Generative Adversarial Networks through Regularization , 2017, NIPS.
[17] Sebastian Nowozin,et al. f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization , 2016, NIPS.
[18] A. Müller. Integral Probability Metrics and Their Generating Classes of Functions , 1997, Advances in Applied Probability.
[19] P. McCullagh,et al. Monograph on Statistics and Applied Probability , 1989 .
[20] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[21] Bernhard Schölkopf,et al. A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..
[22] Richard S. Zemel,et al. Generative Moment Matching Networks , 2015, ICML.
[23] Karthik Sridharan,et al. Optimization, Learning, and Games with Predictable Sequences , 2013, NIPS.
[24] Han Liu,et al. Continual Learning in Generative Adversarial Nets , 2017, ArXiv.
[25] Léon Bottou,et al. Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.
[26] David Pfau,et al. Connecting Generative Adversarial Networks and Actor-Critic Methods , 2016, ArXiv.
[27] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[28] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[29] Xiaogang Wang,et al. Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[30] Manfred K. Warmuth,et al. Exponentiated Gradient Versus Gradient Descent for Linear Predictors , 1997, Inf. Comput..
[31] J. Zico Kolter,et al. Gradient descent GAN optimization is locally stable , 2017, NIPS.
[32] Elad Hazan,et al. The computational power of optimization in online learning , 2015, STOC.
[33] Yingyu Liang,et al. Generalization and Equilibrium in Generative Adversarial Nets (GANs) , 2017, ICML.
[34] Constantinos Daskalakis,et al. Near-optimal no-regret algorithms for zero-sum games , 2011, SODA '11.