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Parametric adversarial divergences, which are a generalization of the losses used to train generative adversarial networks (GANs), have often been described as being approximations of their nonparametric counterparts, such as the Jensen-Shannon divergence, which can be derived under the so-called optimal discriminator assumption.
The hardness of approximate optima in lattices, codes, and systems of linear equations
Sanjeev Arora, László Babai, Jacques Stern, and Z Sweedyk · 1993
Earlier work this paper cites.
Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms
Michael Collins · 2002
Earlier work this paper cites.
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Peter L Bartlett, Michael I Jordan, and Jon D McAuliffe · 2006
Earlier work this paper cites.
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Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang · 2006
Earlier work this paper cites.
A kernel method for the two-sample-problem
Arthur Gretton, Karsten M Borgwardt, Malte Rasch, Bernhard Schölkopf, Alexander J Smola, et al · 2007
Earlier work this paper cites.
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Earlier work this paper cites.
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Bharath K Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, Gert RG Lanckriet, et al · 2012
Earlier work this paper cites.
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Diederik P Kingma and Max Welling · 2014
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Jean-David Benamou, Guillaume Carlier, Marco Cuturi, Luca Nenna, and Gabriel Peyré · 2015
Earlier work this paper cites.
Mathematical Statistics: Basic Ideas and Selected Topics, Volume I
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