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Marc G. Bellemare | Rémi Munos | Stephan Hoyer | Ivo Danihelka | Shakir Mohamed | Balaji Lakshminarayanan | Will Dabney | Ivo Danihelka | R. Munos | Will Dabney | S. Mohamed | Balaji Lakshminarayanan | Stephan Hoyer
[1] V. Zolotarev. METRIC DISTANCES IN SPACES OF RANDOM VARIABLES AND THEIR DISTRIBUTIONS , 1976 .
[2] D. Freedman,et al. Some Asymptotic Theory for the Bootstrap , 1981 .
[3] M. J. Sobel,et al. Discounted MDP's: distribution functions and exponential utility maximization , 1987 .
[4] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[5] A. Müller. Integral Probability Metrics and Their Generating Classes of Functions , 1997, Advances in Applied Probability.
[6] Dudley,et al. Real Analysis and Probability: Measurability: Borel Isomorphism and Analytic Sets , 2002 .
[7] Leonidas J. Guibas,et al. The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.
[8] J. Dedecker,et al. The empirical distribution function for dependent variables: asymptotic and nonasymptotic results in ${\mathbb L}^p$ , 2007 .
[9] A. Raftery,et al. Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .
[10] M. Veraar. On Khintchine inequalities with a weight , 2009, 0909.2586.
[11] Bernhard Schölkopf,et al. A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..
[12] Kenji Fukumizu,et al. Equivalence of distance-based and RKHS-based statistics in hypothesis testing , 2012, ArXiv.
[13] S. Rachev,et al. The Methods of Distances in the Theory of Probability and Statistics , 2013 .
[14] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[15] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[16] Zoubin Ghahramani,et al. Training generative neural networks via Maximum Mean Discrepancy optimization , 2015, UAI.
[17] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[18] Xiaogang Wang,et al. Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[19] Ryan P. Adams,et al. Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks , 2015, ICML.
[20] Hossein Mobahi,et al. Learning with a Wasserstein Loss , 2015, NIPS.
[21] Richard S. Zemel,et al. Generative Moment Matching Networks , 2015, ICML.
[22] Maria L. Rizzo,et al. Energy distance , 2016 .
[23] Koray Kavukcuoglu,et al. Pixel Recurrent Neural Networks , 2016, ICML.
[24] A. Kleywegt,et al. Distributionally Robust Stochastic Optimization with Wasserstein Distance , 2016, Math. Oper. Res..
[25] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[26] Klaus-Robert Müller,et al. Wasserstein Training of Restricted Boltzmann Machines , 2016, NIPS.
[27] Vaibhava Goel,et al. McGan: Mean and Covariance Feature Matching GAN , 2017, ICML.
[28] Léon Bottou,et al. Wasserstein GAN , 2017, ArXiv.
[29] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Peter Dayan,et al. Comparison of Maximum Likelihood and GAN-based training of Real NVPs , 2017, ArXiv.
[31] Marc G. Bellemare,et al. A Distributional Perspective on Reinforcement Learning , 2017, ICML.
[32] Yiming Yang,et al. MMD GAN: Towards Deeper Understanding of Moment Matching Network , 2017, NIPS.
[33] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[34] Daniel Kuhn,et al. Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations , 2015, Mathematical Programming.