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Optimal transport distances have found many applications in machine learning for their capacity to compare non-parametric probability distributions.
Probability inequalities for sums of bounded random variables
Hoeffding, W · 1963
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Generalized gradients and applications
Clarke, H. F · 1975
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Optimization and nonsmooth analysis
Clarke, F. H · 1990
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Kantorovich-rubinstein norm and its application in the theory of lipschitz spaces
Hanin, L · 1992
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Database for handwritten text recognition research
Hull, J · 1994
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Nonlinear programming
Bertsekas, D. P · 1997
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On minimum kantorovich distance estimators
Bassetti, F., Bodini, A., and Regazzini, E · 2006
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Large-scale machine learning with stochastic gradient descent
Bottou, L · 2010
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The optimal partial transport problem
Figalli, A · 2010
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A new transportation distance between non-negative measures, with applications to gradients flows with dirichlet boundary conditions
Figalli, A. and Gigli, N · 2010
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MNIST handwritten digit database
LeCun, Y. and Cortes, C · 2010
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M · 2013
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On properties of the generalized wasserstein distance, 2014
Piccoli, B. and Rossi, F · 2014
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Sgd algorithms based on incomplete u-statistics: Large-scale minimization of empirical risk
Papa, G., Clémençon, S., and Bellet, A · 2015
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Entropic approximation of wasserstein gradient flows
Peyré, G · 2015
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Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., March, M., and Lempitsky, V · 2016
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Sliced wasserstein kernels for probability distributions
Kolouri, S., Zou, Y., and Rohde, G. K · 2016
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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The cramer distance as a solution to biased wasserstein gradients
Bellemare, M. G., Danihelka, I., Dabney, W., Mohamed, S., Lakshminarayanan, B., Hoyer, S., and Munos, R · 2017
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Joint distribution optimal transportation for domain adaptation
Courty, N., Flamary, R., Habrard, A., and Rakotomamonjy, A · 2017
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Optimal transport for domain adaptation
Courty, N., Flamary, R., Tuia, D., and Rakotomamonjy, A · 2017
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Pot python optimal transport library, 2017
Flamary, R. and Courty, N · 2017
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
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Optimal entropy-transport problems and a new hellinger–kantorovich distance between positive measures
Liero, M., Mielke, A., and Savaré, G · 2017
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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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Visda: The visual domain adaptation challenge
Peng, X., Usman, B., Kaushik, N., Hoffman, J., Wang, D., and Saenko, K · 2017
U-statistics : theory and practice / a. j. lee
J Lee, A · 2019
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Wasserstein distributionally robust optimization: Theory and applications in machine learning
Kuhn, D., Mohajerin Esfahani, P., Nguyen, V. A., and Shafieezadeh Abadeh, S · 2019
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Sliced-Wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions
Liutkus, A., Simsekli, U., Majewski, S., Durmus, A., and Stöter, F.-R · 2019
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When does label smoothing help?
Müller, R., Kornblith, S., and Hinton, G. E · 2019
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Subspace robust wasserstein distances
Paty, F.-P. and Cuturi, M · 2019
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Computational optimal transport
Peyré, G. and Cuturi, M · 2019
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A framework for wasserstein-1-type metrics
Schmitzer, B. and Wirth, B · 2017
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Deep hashing network for unsupervised domain adaptation
Venkateswara, H., Eusebio, J., Chakraborty, S., and Panchanathan, S · 2017
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Partial adversarial domain adaptation
Cao, Z., Ma, L., Long, M., and Wang, J · 2018
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Scaling algorithms for unbalanced optimal transport problems
Chizat, L., Peyré, G., Schmitzer, B., and Vialard, F · 2018
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DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation
Damodaran, B. B., Kellenberger, B., Flamary, R., Tuia, D., and Courty, N · 2018
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Learning generative models with sinkhorn divergences
Genevay, A., Peyre, G., and Cuturi, M · 2018
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Sinkhorn divergences for unbalanced optimal transport
Séjourné, T., Feydy, J., Vialard, F.-X., Trouvé, A., and Peyré, G · 2019
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Optimal transport: Fast probabilistic approximation with exact solvers
Sommerfeld, M., Schrieber, J., Zemel, Y., and Munk, A · 2019
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Robust optimal transport with applications in generative modeling and domain adaptation
Balaji, Y., Chellappa, R., and Feizi, S · 2020
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Partial optimal transport with applications on positive-unlabeled learning
Chapel, L., Alaya, M. Z., and Gasso, G · 2020
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Adversarial-learned loss for domain adaptation
Chen, M., Zhao, S., Liu, H., and Cai, D · 2020
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Stochastic subgradient method converges on tame functions
Davis, D., Drusvyatskiy, D., Kakade, S., and Lee, J. D · 2020
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A swiss army knife for minimax optimal transport
Dhouib, S., Redko, I., Kerdoncuff, T., Emonet, R., and Sebban, M · 2020
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Learning with minibatch wasserstein : asymptotic and gradient properties
Fatras, K., Zine, Y., Flamary, R., Gribonval, R., and Courty, N · 2020
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A balanced and uncertainty-aware approach for partial domain adaptation
Jian, L., Yunbo, W., Dapeng, H., Ran, H., and Jiashi, F · 2020
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Outlier-robust optimal transport
Mukherjee, D., Guha, A., Solomon, J., Sun, Y., and Yurochkin, M · 2020
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Missing data imputation using optimal transport
Muzellec, B., Josse, J., Boyer, C., and Cuturi, M · 2020
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Unbalanced optimal transport using integral probability metric regularization
Nath, J. S · 2020
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On unbalanced optimal transport: An analysis of Sinkhorn algorithm
Pham, K., Le, K., Ho, N., Pham, T., and Bui, H · 2020
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Minibatch optimal transport distances; analysis and applications
Fatras, K., Zine, Y., Majewski, S., Flamary, R., Gribonval, R., and Courty, N · 2021
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