Nonparametric Density Estimation under Adversarial Losses release_bu34qw73cjdovoc2lb3vc2ob7y

by Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer, Barnabás Póczos


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Showing 1 - 30 of 44 references (in 33ms)

via grobid
Ehsan Abbasnejad, Javen Shi, and Anton van den Hengel. Deep lipschitz networks and dudley GANs, 2018. URL

via fuzzy
Scale-sensitive dimensions, uniform convergence, and learnability
N. Alon, S. Ben-David, N. Cesa-Bianchi, D. Haussler
IEEE Annual Symposium on Foundations of Computer Science
doi:10.1109/sfcs.1993.366858  dblp:conf/focs/AlonBCH93 [PDF]

via fuzzy
Two-Sample Test Statistics for Measuring Discrepancies Between Two Multivariate Probability Density Functions Using Kernel-Based Density Estimates
N.H. Anderson, P. Hall, D.M. Titterington
1994   Journal of Multivariate Analysis
doi:10.1006/jmva.1994.1033 [PDF]

via grobid
Martin Anthony and Peter L Bartlett. Neural network learning: Theoretical foundations. cambridge university press, 2009.

via fuzzy
Wasserstein Generative Adversarial Networks
Martín Arjovsky, Soumith Chintala, Léon Bottou
2017   International Conference on Machine Learning

via grobid
Nachman Aronszajn. Theory of reproducing kernels. Transactions of the American mathematical society, 68(3):337-404, 1950.

via fuzzy
Generalization and Equilibrium in Generative Adversarial Nets (GANs) [article]
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang
2017    pre-print
version:v1  arXiv:1703.00573v1 [PDF]

via grobid
Alain Berlinet and Christine Thomas-Agnan. Reproducing kernel Hilbert spaces in probability and statistics. Springer Science & Business Media, 2011.

via grobid
Gérard Biau and Luc Devroye. Lectures on the nearest neighbor method. Springer, 2015.

via grobid
Geometrical Insights for Implicit Generative Modeling [article]
Leon Bottou and Martin Arjovsky and David Lopez-Paz and Maxime Oquab
2017    pre-print
version:v1  arXiv:1712.07822v1 [PDF]

via grobid
Lawrence D Brown, Cun-Hui Zhang, et al. Asymptotic nonequivalence of nonparametric experiments when the smoothness index is 1/2. The Annals of Statistics, 26(1):279-287, 1998.

via grobid
Guillermo Canas and Lorenzo Rosasco. Learning probability measures with respect to optimal transport metrics. In Advances in Neural Information Processing Systems, pages 2492-2500, 2012.

via grobid
Siddhartha Chib and Edward Greenberg. Understanding the Metropolis-Hastings algorithm. The american statistician, 49(4):327-335, 1995.

via fuzzy
Monte Carlo Methods of Inference for Implicit Statistical Models
Peter J. Diggle, Richard J. Gratton
1984   Journal of the Royal Statistical Society: Series B (Methodological)

via grobid
David L Donoho, Iain M Johnstone, Gérard Kerkyacharian, and Dominique Picard. Density estimation by wavelet thresholding. The Annals of Statistics, pages 508-539, 1996.

via fuzzy
Hypothesis Transfer Learning via Transformation Functions [article]
Simon Shaolei Du, Jayanth Koushik, Aarti Singh, Barnabas Poczos
2016    pre-print
version:v1  arXiv:1612.01020v1 [PDF]

via grobid
RM Dudley. Speeds of metric probability convergence. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete, 22(4):323-332, 1972.

via grobid
Training generative neural networks via Maximum Mean Discrepancy optimization [article]
Gintare Karolina Dziugaite and Daniel M. Roy and Zoubin Ghahramani
2015    pre-print
version:v1  arXiv:1505.03906v1 [PDF]

via grobid
Sam Efromovich. Orthogonal series density estimation. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4):467-476, 2010.

via grobid
Dominik Maria Endres and Johannes E Schindelin. A new metric for probability distributions. IEEE Transactions on Information theory, 49(7):1858-1860, 2003.

via grobid
David Pollard. Empirical processes: theory and applications. In NSF-CBMS regional conference series in probability and statistics, pages i-86. JSTOR, 1990.

via grobid
Novi Quadrianto, James Petterson, and Alex J Smola. Distribution matching for transduction. In Advances in Neural Information Processing Systems, 2009.

via fuzzy
On the Decreasing Power of Kernel and Distance based Nonparametric Hypothesis Tests in High Dimensions
Sashank J. Reddi, Aaditya Ramdas, Barnabas Poczos, Aarti Singh, Larry Wasserman
doi:10.1184/r1/6476201 [PDF]

via fuzzy
On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests
Aaditya Ramdas, Nicolás Trillos, Marco Cuturi
2017   Entropy
doi:10.3390/e19020047 [PDF]

via fuzzy
Stochastic Backpropagation and Approximate Inference in Deep Generative Models [article]
Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra
2014    pre-print
version:v1  arXiv:1401.4082v1 [PDF]

via grobid
Christian P Robert. Monte Carlo methods. Wiley Online Library, 2004.

via grobid
Minimax Distribution Estimation in Wasserstein Distance [article]
Shashank Singh, Barnabás Póczos
2018    pre-print
version:v1  arXiv:1802.08855v1 [PDF]

via grobid
Minimax Estimation of Quadratic Fourier Functionals [article]
Shashank Singh, Bharath K. Sriperumbudur, Barnabás Póczos
2018    pre-print
version:v1  arXiv:1803.11451v1 [PDF]

via grobid
Nickolay Smirnov. Table for estimating the goodness of fit of empirical distributions. The annals of mathematical statistics, 19(2):279-281, 1948.

via grobid
Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, and Revant Kumar. Density estimation in infinite dimensional exponential families. The Journal of Machine Learning Research, 18(1): 1830-1888, 2017.
Showing 1 - 30 of 44 references  next »