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The family of f-divergences is ubiquitously applied to generative modeling in order to adapt the distribution of the model to that of the data.
The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming
Bregman, L. M · 1967
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Worst-case loss bounds for single neurons
Helmbold, D. P., Kivinen, J., and Warmuth, M. K · 1995
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Exponentially many local minima for single neurons
Auer, P., Herbster, M., and Warmuth, M. K · 1996
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Relative loss bounds for multidimensional regression problems
Kivinen, J. and Warmuth, M. K · 1998
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From Brunn-Minkowski to Brascamp-Lieb and to logarithmic Sobolev inequalities
Bobkov, S. and Ledoux, M · 2000
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Clustering with Bregman divergences
Banerjee, A., Merugu, S., Dhillon, I. S., and Ghosh, J · 2005
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Information geometry of divergence functions
Amari, S.-i. and Cichocki, A · 2010
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Skew Jensen-Bregman Voronoi diagrams
Nielsen, F. and Nock, R · 2011
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
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On Bregman distances and divergences of probability measures
Stummer, W. and Vajda, I · 2012
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Density ratio estimation in machine learning
Sugiyama, M., Suzuki, T., and Kanamori, T · 2012
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Bregman divergences and triangle inequality
Acharyya, S., Banerjee, A., and Boley, D · 2013
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Convex relaxations of Bregman divergence clustering
Cheng, H., Zhang, X., and Schuurmans, D · 2013
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Some decision procedures based on scaled Bregman distance surfaces
Kißlinger, A. and Stummer, W · 2013
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Generative adversarial nets
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. C., and Bengio, Y · 2014
Cited alongside, same era.
f-GAN: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R · 2016
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Wasserstein continuity of entropy and outer bounds for interference channels
Polyanskiy, Y. and Wu, Y · 2016
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Towards principled methods for training generative adversarial networks
Arjovsky, M. and Bottou, L · 2017
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Arjovsky, M., Chintala, S., and Bottou, L · 2017
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GANS trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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MMD GAN: Towards deeper understanding of moment matching network
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
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b-GAN: Unified framework of generative adversarial networks
Uehara, M., Sato, I., Suzuki, M., Nakayama, K., and Matsuo, Y
Cited in the paper.
Generative adversarial nets from a density ratio estimation perspective
Uehara, M., Sato, I., Suzuki, M., Nakayama, K., and Matsuo, Y
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Li, C.-L., Chang, W.-C., Cheng, Y., Yang, Y., and Póczos, B · 2017
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Fisher gan
Mroueh, Y. and Sercu, T · 2017
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VEEGAN: reducing mode collapse in GANs using implicit variational learning
Srivastava, A., Valkov, L., Russell, C., Gutmann, M. U., and Sutton, C. A · 2017
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