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The Wasserstein probability metric has received much attention from the machine learning community.
Metric distances in spaces of random variables and their distributions
Zolotarev, V. M. (1976) · 1976
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Some asymptotic theory for the bootstrap
Bickel, P. J. and Freedman, D. A. (1981) · 1981
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Discounted MDP’s: Distribution functions and exponential utility maximization
Chung, K.-J. and Sobel, M. J. (1987) · 1987
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Elements of information theory
Cover, T. M. and Thomas, J. A. (1991) · 1991
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Integral probability metrics and their generating classes of functions
Müller, A. (1997) · 1997
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The earth mover’s distance as a metric for image retrieval
Rubner, Y., Tomasi, C., and Guibas, L. J. (2000) · 2000
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Real analysis and probability
Dudley, R. M. (2002) · 2002
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E-statistics: The energy of statistical samples
Székely, G. J. (2002) · 2002
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The empirical distribution function for dependent variables: asymptotic and nonasymptotic results in Lp
Dedecker, J. and Merlevède, F. (2007) · 2007
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Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. E. (2007) · 2007
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On Khintchine inequalities with a weight
Veraar, M. (2010) · 2010
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A. (2012) · 2012
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UCI machine learning repository
Lichman, M. (2013) · 2013
Cited alongside, same era.
The methods of distances in the theory of probability and statistics
Rachev, S. T., Klebanov, L., Stoyanov, S. V., and Fabozzi, F. (2013) · 2013
Cited alongside, same era.
Equivalence of distance-based and RKHS-based statistics in hypothesis testing
Sejdinovic, D., Sriperumbudur, B., Gretton, A., Fukumizu, K., et al. (2013) · 2013
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
Cited alongside, same era.
Auto-encoding variational Bayes
Kingma, D. P. and Welling, M. (2014) · 2014
Cited alongside, same era.
Training generative neural networks via maximum mean discrepancy optimization
Dziugaite, G. K., Roy, D. M., and Ghahramani, Z. (2015) · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T. (2015) · 2015
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Distributionally robust stochastic optimization with Wasserstein distance
Gao, R. and Kleywegt, A. J. (2016) · 2016
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Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A. (2016) · 2016
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Wasserstein training of restricted Boltzmann machines
Montavon, G., Müller, K.-R., and Cuturi, M. (2016) · 2016
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Energy distance
Rizzo, M. L. and Székely, G. J. (2016) · 2016
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Pixel recurrent neural networks
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Esfahani, P. M. and Kuhn, D. (2015) · 2015
Cited alongside, same era.
Learning with a Wasserstein loss
Frogner, C., Zhang, C., Mobahi, H., Araya, M., and Poggio, T. A. (2015) · 2015
Cited alongside, same era.
Probabilistic backpropagation for scalable learning of Bayesian neural networks
Hernández-Lobato, J. M. and Adams, R. P. (2015) · 2015
Cited alongside, same era.
Generative moment matching networks
Li, Y., Swersky, K., and Zemel, R. (2015) · 2015
Cited alongside, same era.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X. (2015) · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S. (2015) · 2015
Cited alongside, same era.
Van den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K. (2016) · 2016
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Arjovsky, M., Chintala, S., and Bottou, L. (2017) · 2017
Closest in time.
A distributional perspective on reinforcement learning
Bellemare, M. G., Dabney, W., and Munos, R. (2017) · 2017
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Comparison of Maximum Likelihood and GAN-based training of Real NVPs
Danihelka, I., Lakshminarayanan, B., Uria, B., Wierstra, D., and Dayan, P. (2017) · 2017
Closest in time.
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017) · 2017
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MMD GAN: Towards deeper understanding of moment matching network
Li, C.-L., Chang, W.-C., Cheng, Y., Yang, Y., and Póczos, B. (2017) · 2017
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McGan: Mean and covariance feature matching GAN
Mroueh, Y., Sercu, T., and Goel, V. (2017) · 2017
Closest in time.