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Given samples from two joint distributions, we consider the problem of Optimal Transportation (OT) between them when conditioned on a common variable.
On the transfer of masses (in russian)
Kantorovich, L. (1942) · 1942
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al. (2009) · 2009
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Learning to detect unseen object classes by between-class attribute transfer
Lampert, C. H., Nickisch, H., and Harmeling, S. (2009) · 2009
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Hilbert space embeddings of conditional distributions with applications to dynamical systems
Song, L., Huang, J., Smola, A., and Fukumizu, K. (2009) · 2009
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MNIST handwritten digit database
LeCun, Y. and Cortes, C. (2010) · 2010
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Universality, characteristic kernels and RKHS embedding of measures
Sriperumbudur, B. K., Fukumizu, K., and Lanckriet, G. R. G. (2011) · 2011
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Conditional mean embeddings as regressors
Grünewälder, S., Lever, G., Gretton, A., Baldassarre, L., Patterson, S., and Pontil, M. (2012) · 2012
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Learning with a wasserstein loss
Frogner, C., Zhang, C., Mobahi, H., Araya, M., and Poggio, T. A. (2015) · 2015
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A vector-contraction inequality for rademacher complexities
Maurer, A. (2016) · 2016
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Enriching word vectors with subword information
Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. (2017) · 2017
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Atlantic causal inference conference (ACIC) data analysis challenge 2017
Hahn, P. R., Dorie, V., and Murray, J. S. (2019) · 2017
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Kernel mean embedding of distributions: A review and beyond
Muandet, K., Fukumizu, K., Sriperumbudur, B., and Schölkopf, B. (2017) · 2017
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Implicit regularization in deep learning
Neyshabur, B. (2017) · 2017
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Drug and disease signature integration identifies synergistic combinations in glioblastoma
Stathias, V., Jermakowicz, A. M., Maloof, M. E., Forlin, M., Walters, W. M., Suter, R. K., Durante, M. A., Williams, S. L., Harbour, J. W., Volmar, C.-H., Lyons, N. J., Wahlestedt, C., Graham, R. M., Ivan, M. E., Komotar, R. J., Sarkaria, J. N., Subramanian, A., Golub, T. R., Schürer, S. C., and Ayad, N. G. (2018) · 2018
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Scanpy: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., and Theis, F. J. (2018) · 2018
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Obtaining fairness using optimal transport theory
Gordaliza, P., Barrio, E. D., Fabrice, G., and Loubes, J.-M. (2019) · 2019
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Helber, P., Bischke, B., Dengel, A., and Borth, D. (2019) · 2019
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Efficient robust optimal transport with application to multi-label classification
Jawanpuria, P., Satyadev, N., and Mishra, B. (2021) · 2021
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Wasserstein generative learning of conditional distribution
Liu, S., Zhou, X., Jiao, Y., and Huang, J. (2021) · 2021
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Conditional bures metric for domain adaptation
Luo, Y.-W. and Ren, C.-X. (2021) · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I. (2021) · 2021
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Data driven conditional optimal transport
Tabak, E. G., Trigila, G., and Zhao, W. (2021) · 2021
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Supervised training of conditional monge maps
Bunne, C., Krause, A., and Cuturi, M. (2022) · 2022
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Computational optimal transport
Peyré, G. and Cuturi, M. (2019) · 2019
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Learning with minibatch wasserstein: asymptotic and gradient properties
Fatras, K., Zine, Y., Flamary, R., Gribonval, R., and Courty, N. (2020) · 2020
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Minimax risk and uniform convergence rates for nonparametric dyadic regression
Graham, B. S., Niu, F., and Powell, J. L. (2020) · 2020
Cited alongside, same era.
Universal Approximation with Deep Narrow Networks
Kidger, P. and Lyons, T. (2020) · 2020
Cited alongside, same era.
Semantic correspondence as an optimal transport problem
Liu, Y., Zhu, L., Yamada, M., and Yang, Y. (2020) · 2020
Cited alongside, same era.
Learning single-cell perturbation responses using neural optimal transport
Bunne, C., Stark, S. G., Gut, G., del Castillo, J. S., Lehmann, K.-V., Pelkmans, L., Krause, A., and Rätsch, G. (2021) · 2021
Cited alongside, same era.
Unbalanced minibatch optimal transport; applications to domain adaptation
Fatras, K., Séjourné, T., Courty, N., and Flamary, R. (2021) · 2021
Cited alongside, same era.
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Otkge: Multi-modal knowledge graph embeddings via optimal transport
Cao, Z., Xu, Q., Yang, Z., He, Y., Cao, X., and Huang, Q. (2022) · 2022
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Minimax optimal conditional density estimation under total variation smoothness
Li, M., Neykov, M., and Balakrishnan, S. (2022) · 2022
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Training lipschitz continuous operators using reproducing kernels
Waarde, H. v. and Sepulchre, R. (2022) · 2022
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Tip-Adapter: Training-free adaption of clip for few-shot classification
Zhang, R., Zhang, W., Fang, R., Gao, P., Li, K., Dai, J., Qiao, Y., and Li, H. (2022) · 2022
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Learning single-cell perturbation responses using neural optimal transport
Bunne, C., Stark, S. G., Gut, G., del Castillo, J. S., Levesque, M., Lehmann, K.-V., Pelkmans, L., Krause, A., and Ratsch, G. (2023) · 2023
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Prompt learning with optimal transport for vision-language models
Chen, G., Yao, W., Song, X., Li, X., Rao, Y., and Zhang, K. (2023) · 2023
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