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Approximate Bayesian Computation (ABC) is a popular method for approximate inference in generative models with intractable but easy-to-sample likelihood.
“Inferring coalescence times from dna sequence data,”
S. Tavaré, D. J. Balding, R. C. Griffiths, and P. Donnelly, · 1997
Earlier work this paper cites.
Mass transportation problems. Vol. I
S. T. Rachev and L. Rüschendorf, · 1998
Earlier work this paper cites.
“A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,”
D. Martin, C. Fowlkes, D. Tal, J. Malik, et al., · 2001
Earlier work this paper cites.
“Approximate bayesian computation in population genetics,”
M. A. Beaumont, W. Zhang, and D. J. Balding, · 2002
Earlier work this paper cites.
“A non-local algorithm for image denoising,”
A. Buades, B. Coll, and J.-M. Morel, · 2005
Earlier work this paper cites.
“Bayesian inference, monte carlo sampling and operational risk,”
G. Peters and S. Sisson, · 2006
Earlier work this paper cites.
“Using approximate bayesian computation to estimate tuberculosis transmission parameters from genotype data,”
M. Tanaka, A. Francis, F. Luciani, and S. Sisson, · 2006
Earlier work this paper cites.
Optimal Transport: Old and New
C. Villani, · 2008
Earlier work this paper cites.
“Approximate bayesian computation scheme for parameter inference and model selection in dynamical systems,”
T. Toni, D. Welch, N. Strelkowa, A. Ipsen, and M. Stumpf, · 2009
Earlier work this paper cites.
“Statistical inference for noisy nonlinear ecological dynamic systems,”
S. N. Wood, · 2010
Earlier work this paper cites.
“Constructing summary statistics for approximate bayesian computation: semi-automatic approximate bayesian computation,”
P. Fearnhead and D. Prangle, · 2012
Cited alongside, same era.
“Wasserstein barycenter and its application to texture mixing,”
J. Rabin, G. Peyré, J. Delon, and M. Bernot, · 2012
Cited alongside, same era.
Unidimensional and Evolution Methods for Optimal Transportation
N. Bonnotte, · 2013
Cited alongside, same era.
“Sliced and Radon Wasserstein barycenters of measures,”
N. Bonneel, J. Rabin, G. Peyré, and H. Pfister, · 2015
Cited alongside, same era.
“K2-abc: Approximate bayesian computation with kernel embeddings,”
M. Park, W. Jitkrittum, and D. Sejdinovic, · 2016
Cited alongside, same era.
“Approximate bayesian computation with kullback-leibler divergence as data discrepancy,”
B. Jiang, T.-Y. Wu, and W.-H. Wong, · 2018
“Approximate bayesian computation with the wasserstein distance,”
E. Bernton, P. E. Jacob, M. Gerber, and C. P. Robert, · 2019
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“Sharp asymptotic and finite-sample rates of convergence of empirical measures in wasserstein distance,”
J. Weed and F. Bach, · 2019
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“Sliced-Wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions,”
A. Liutkus, U. Şimşekli, S. Majewski, A. Durmus, and F.-R. Stoter, · 2019
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“Sliced Wasserstein auto-encoders,”
S. Kolouri, P. E. Pope, C. E. Martin, and G. K. Rohde, · 2019
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“Max-Sliced Wasserstein distance and its use for GANs,”
I. Deshpande, Y.-T. Hu, R. Sun, A. Pyrros, N. Siddiqui, S. Koyejo, Z. Zhao, D. Forsyth, and A. Schwing, · 2019
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“Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance,”
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Cited alongside, same era.
“Generative modeling using the sliced Wasserstein distance,”
I. Deshpande, Z. Zhang, and A. G. Schwing, · 2018
Cited alongside, same era.
“Overview of Approximate Bayesian Computation,”
S. A. Sisson, Y. Fan, and M. A. Beaumont, · 2018
Cited alongside, same era.
“Asymptotic properties of approximate Bayesian computation,”
D. T. Frazier, G. M. Martin, C.P. Robert, and J. Rousseau, · 2018
Cited alongside, same era.
“pyABC: distributed, likelihood-free inference,”
E. Klinger, D. Rickert, and J. Hasenauer, · 2018
Cited alongside, same era.
“Sw-abc software implementation,” https://github.com/kimiandj/slicedwass_abc , https://vdeborto.github.io/publication/sw_abc
K. Nadjahi, V. De Bortoli, A. Durmus, R. Badeau, and U. Şimşekli,
Cited in the paper.
K. Nadjahi, A. Durmus, U. Şim · 2019
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“Computational optimal transport,”
G. Peyré, M. Cuturi, et al., · 2019
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“Brief review of image denoising techniques,”
L. Fan, F. Zhang, H. Fan, and C. Zhang, · 2019
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“Generalized Sliced Wasserstein Distances,”
S. Kolouri, K. Nadjahi, U. Simsekli, R. Badeau, and G. K. Rohde, · 2019
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