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The estimation of an f-divergence between two probability distributions based on samples is a fundamental problem in statistics and machine learning.
Alpha divergence for classification, indexing and retrieval
A. O. Hero, B. Ma, O. Michel, and J. Gorman · 2001
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Applications of entropic spanning graphs
A. O. Hero, B. Ma, O. J. J. Michel, and J. Gorman · 2002
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A new class of metric divergences on probability spaces and its applicability in statistics
Ferdinand Osterreicher and Igor Vajda · 2003
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Information theory and statistics: A tutorial
Imre Csiszár, Paul C Shields, et al · 2004
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Hilbertian metrics and positive definite kernels on probability measures
M. Hein and O. Bousquet · 2005
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Statistical inference based on divergence measures
Leandro Pardo · 2005
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On divergences and informations in statistics and information theory
Friedrich Liese and Igor Vajda · 2006
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2007
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Approximating the kullback leibler divergence between gaussian mixture models
John R Hershey and Peder A Olsen · 2007
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Kullback-leibler divergence estimation of continuous distributions
F. Perez-Cruz · 2008
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Introduction to nonparametric estimation
Alexandre B. Tsybakov · 2009
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Divergence estimation for multidimensional densities via k-nearest-neighbor distances
Q. Wang, S. R. Kulkarni, and S. Verdú · 2009
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Estimating divergence functionals and the likelihood ratio by convex risk minimization
XuanLong Nguyen, Martin J. Wainwright, and Michael I. Jordan · 2010
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On the estimation of alpha-divergences
B. Poczos and J. Schneider · 2011
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Lower and upper bounds for approximation of the kullback-leibler divergence between gaussian mixture models
J-L Durrieu, J-Ph Thiran, and Finnian Kelly · 2012
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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f-divergence estimation and two-sample homogeneity test under semiparametric density-ratio models
T. Kanamori, T. Suzuki, and M. Sugiyama · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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ELBO surgery: yet another way to carve up the variational evidence lower bound
Matthew D Hoffman and Matthew J Johnson · 2016
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Rényi divergence variational inference
Yingzhen Li and Richard E Turner · 2016
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f-GAN: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Variational inference via
Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, and David Blei · 2017
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GANs trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Nonparametric estimation of Rényi divergence and friends
A. Krishnamurthy, A. Kandasamy, B. Póczos, and L. Wasserman · 2014
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Ensemble estimation of multivariate f-divergence
K. Moon and A. Hero · 2014
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Multivariate f-divergence estimation with confidence
K. Moon and A. Hero · 2014
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On the chi square and higher-order chi distances for approximating f-divergences
Frank Nielsen and Richard Nock · 2014
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Generalized exponential concentration inequality for Rényi divergence estimation
S. Singh and B. Poczos · 2014
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Importance weighted autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2015
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Fixing a broken ELBO
Alexander Alemi, Ben Poole, Ian Fischer, Joshua Dillon, Rif A Saurous, and Kevin Murphy · 2018
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Variational inference and model selection with generalized evidence bounds
Liqun Chen, Chenyang Tao, Ruiyi Zhang, Ricardo Henao, and Lawrence Carin Duke · 2018
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Isolating sources of disentanglement in variational autoencoders
Tian Qi Chen, Xuechen Li, Roger Grosse, and David Duvenaud · 2018
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Avoiding latent variable collapse with generative skip models
Adji B Dieng, Yoon Kim, Alexander M Rush, and David M Blei · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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On variational lower bounds of mutual information
Ben Poole, Sherjil Ozair, Aäron van den Oord, Alexander A Alemi, and George Tucker · 2018
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Wasserstein auto-encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2018
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