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Classical optimal transport problem seeks a transportation map that preserves the total mass betwenn two probability distributions, requiring their mass to be the same.
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Parallel unbalanced optimal transport regularization for large scale imaging problems
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An algorithm for quadratic programming
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Approximate methods in optimization problems
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Extended kantorovich norms: a tool for optimization
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On the geometry of metric measure spaces
Sturm, K. T. (2006) · 2006
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Caltech-256 object category dataset
Griffin, G., A. Holub, and P. Perona (2007) · 2007
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Learning classifiers from only positive and unlabeled data
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Optimal Transport: Old and New
Villani, C. (2009) · 2009
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Free boundaries in optimal transport and monge-ampère obstacle problems
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The optimal partial transport problem
Figalli, A. (2010) · 2010
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Adapting visual category models to new domains
Saenko, K., B. Kulis, M. Fritz, and T. Darrell (2010) · 2010
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Displacement interpolation using lagrangian mass transport
Bonneel, N., M. Van de Panne, S. Paris, and W. Heidrich (2011) · 2011
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Gromov–Wasserstein distances and the metric approach to object matching
Mémoli, F. (2011) · 2011
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Positive and unlabeled learning for graph classification
Zhao, Y., X. Kong, and S. Philip (2011) · 2011
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Subgraph matching kernels for attributed graphs
Kriege, N. and P. Mutzel (2012) · 2012
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M. (2013) · 2013
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Regularized discrete optimal transport
Ferradans, S., N. Papadakis, J. Rabin, G. Peyré, and J. F. Aujol (2013) · 2013
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Revisiting Frank-Wolfe: Projection-free sparse convex optimization
Jaggi, M. (2013) · 2013
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Decaf: A deep convolutional activation feature for generic visual recognition
Donahue, J., Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell (2014) · 2014
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Analysis of learning from positive and unlabeled data
Du Plessis, M., G. Niu, and M. Sugiyama (2014) · 2014
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Multilevel clustering via wasserstein means
Ho, N., X. L. Nguyen, M. Yurochkin, H. H. Bui, V. Huynh, and D. Phung (2017) · 2017
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Class-prior estimation for learning from positive and unlabeled data
Plessis, M. C., G. Niu, and M. Sugiyama (2017, April) · 2017
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Learning from positive and unlabeled data under the selected at random assumption
Bekker, J. and J. Davis (2018) · 2018
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Scaling algorithms for unbalanced optimal transport problems
Chizat, L., G. Peyré, B. Schmitzer, and F.-X. Vialard (2018) · 2018
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Vayer, T., L. Chapel, R. Flamary, R. Tavenard, and N. Courty (2018) · 2018
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Iterative Bregman projections for regularized transportation problems
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On the global linear convergence of frank-wolfe optimization variants
Lacoste-Julien, S. and M. Jaggi (2015) · 2015
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Convolutional wasserstein distances: Efficient optimal transportation on geometric domains
Solomon, J., F. de Goes, G. Peyré, M. Cuturi, A. Butscher, A. Nguyen, T. Du, and L. Guibas (2015) · 2015
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Convergence rate of Frank-Wolfe for non-convex objectives
Lacoste-Julien, S. (2016) · 2016
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Gromov-Wasserstein averaging of kernel and distance matrices
Peyré, G., M. Cuturi, and J. Solomon (2016) · 2016
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Wasserstein Generative Adversarial Networks
Arjovsky, M., S. Chintala, and L. Bottou (2017) · 2017
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Bonneel, N. and D. Coeurjolly (2019) · 2019
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A statistical test of isomorphism between metric-measure spaces using the distance-to-a-measure signature
Brécheteau, C. (2019) · 2019
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Classification from positive, unlabeled and biased negative data
Hsieh, Y., G. Niu, and M. Sugiyama (2019) · 2019
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Learning from positive and unlabeled data with a selection bias
Kato, M., T. Teshima, and J. Honda (2019) · 2019
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Computational optimal transport
Peyré, G. and M. Cuturi (2019) · 2019
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Sliced gromov-wasserstein
Vayer, T., R. Flamary, R. Tavenard, L. Chapel, and N. Courty (2019) · 2019
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Scalable unbalanced optimal transport using generative adversarial networks
Yang, K. D. and C. Uhler (2019) · 2019
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Learning from positive and unlabeled data: A survey
Bekker, J. and J. Davis (2020) · 2020
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Mixture proportion estimation via kernel embeddings of distributions
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