R. K. Ahuja, T. L. Magnanti, and J. B. Orlin, Network Flows: Theory, Algorithms, and Applications, 1993.

S. Ben-david, T. Luu, T. Lu, and D. Pál, Impossibility theorems for domain adaptation, Artificial Intelligence and Statistics Conference (AISTATS), pp.129-136, 2010.

J. Benamou and Y. Brenier, A computational fluid mechanics solution to the Monge-Kantorovich mass transfer problem, Numerische Mathematik, vol.84, issue.3, pp.375-393, 2000.
DOI : 10.1007/s002110050002

D. P. Bertsekas, Nonlinear programming, Athena scientific Belmont, 1999.

N. Bonneel, J. Rabin, G. Peyré, and H. Pfister, Sliced and Radon Wasserstein Barycenters of Measures, Journal of Mathematical Imaging and Vision, vol.11, issue.1, pp.22-45, 2015.
DOI : 10.1007/s10851-014-0506-3

URL : https://hal.archives-ouvertes.fr/hal-00881872

N. Bonneel, M. Van-de-panne, S. Paris, and W. Heidrich, Displacement interpolation using Lagrangian mass transport, ACM Transactions on Graphics, vol.30, issue.6, pp.1-15812, 2011.
DOI : 10.1145/2070781.2024192

URL : https://hal.archives-ouvertes.fr/hal-00763270

K. Bredies, D. A. Lorenz, and P. Maass, A generalized conditional gradient method and its connection to an iterative shrinkage method, Computational Optimization and Applications, vol.104, issue.2, pp.173-193, 2009.
DOI : 10.1007/s10589-007-9083-3

K. Bredies, D. Lorenz, and P. Maass, Equivalence of a generalized conditional gradient method and the method of surrogate functionals, 2005.

L. Bruzzone and M. Marconcini, Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.5, pp.770-787, 2010.
DOI : 10.1109/TPAMI.2009.57

T. S. Caetano, T. Caelli, D. Schuurmans, and D. Barone, Graphical Models and Point Pattern Matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.10, pp.1646-1663, 2006.
DOI : 10.1109/TPAMI.2006.207

T. S. Caetano, J. J. Mcauley, L. Cheng, Q. V. Le, and A. J. Smola, Learning Graph Matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.6, pp.1048-1058, 2009.
DOI : 10.1109/TPAMI.2009.28

G. Carlier, A. Oberman, and E. Oudet, Numerical methods for matching for teams and Wasserstein barycenters, ESAIM: Mathematical Modelling and Numerical Analysis, vol.49, issue.6, 2014.
DOI : 10.1051/m2an/2015033

URL : https://hal.archives-ouvertes.fr/hal-00987292

M. Carreira-perpinan and W. Wang, LASS: A simple assignment model with laplacian smoothing, AAAI Conference on Artificial Intelligence, 2014.

N. Courty, R. Flamary, and D. Tuia, Domain Adaptation with Regularized Optimal Transport, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2014.
DOI : 10.1007/978-3-662-44848-9_18

URL : https://hal.archives-ouvertes.fr/hal-01018698

M. Cuturi, Sinkhorn distances: Lightspeed computation of optimal transportation, Neural Information Processing Systems (NIPS), pp.2292-2300, 2013.

M. Cuturi and D. Avis, Ground metric learning, Journal of Machine Learning Research, vol.15, issue.1, pp.533-564, 2014.

M. Cuturi and A. Doucet, Fast computation of Wasserstein barycenters, International Conference on Machine Learning (ICML), 2014.

H. Daumé and I. , Frustratingly easy domain adaptation, Ann. Meeting of the Assoc. Computational Linguistics, 2007.

J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang et al., DeCAF: a deep convolutional activation feature for generic visual recognition, International Conference on Machine Learning (ICML), pp.647-655, 2014.

S. Ferradans, N. Papadakis, J. Rabin, G. Peyré, and J. Aujol, Regularized discrete optimal transport, Scale Space and Variational Methods in Computer Vision, SSVM, pp.428-439, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00797078

W. Gangbo and R. J. Mccann, The geometry of optimal transportation, Acta Mathematica, vol.177, issue.2, pp.113-161, 1996.
DOI : 10.1007/BF02392620

P. Germain, A. Habrard, F. Laviolette, and E. Morvant, A PAC- Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers, International Conference on Machine Learning (ICML), pp.738-746, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00822685

B. Gong, Y. Shi, F. Sha, and K. Grauman, Geodesic flow kernel for unsupervised domain adaptation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2066-2073, 2012.

R. Gopalan, R. Li, and R. Chellappa, Domain adaptation for object recognition: An unsupervised approach, 2011 International Conference on Computer Vision, pp.999-1006, 2011.
DOI : 10.1109/ICCV.2011.6126344

G. Griffin, A. Holub, and P. Perona, Caltech-256 Object Category Dataset, California Institute of Technology, 2007.

J. Ham, D. Lee, and L. Saul, Semisupervised alignment of manifolds, 10th International Workshop on Artificial Intelligence and Statistics, pp.120-127, 2005.

J. Hoffman, E. Rodner, J. Donahue, K. Saenko, and T. Darrell, Efficient learning of domain invariant image representations, International Conference on Learning Representations (ICLR), 2013.

I. Jhuo, D. Liu, D. T. Lee, and S. Chang, Robust visual domain adaptation with low-rank reconstruction, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pp.2168-2175

L. Kantorovich, On the translocation of masses The sinkhorn-knopp algorithm: Convergence and applications, C.R. (Doklady) Acad. Sci. URSS SIAM Journal on Matrix Analysis and Applications, vol.37, issue.30 1, pp.199-201, 1942.

B. Kulis, K. Saenko, and T. Darrell, What you saw is not what you get: Domain adaptation using asymmetric kernel transforms, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995702

A. Kumar, H. Daumé, I. , and D. Jacobs, Generalized multiview analysis: A discriminative latent space, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.

M. Long, J. Wang, G. Ding, J. Sun, and P. Yu, Transfer Feature Learning with Joint Distribution Adaptation, 2013 IEEE International Conference on Computer Vision, pp.2200-2207, 2013.
DOI : 10.1109/ICCV.2013.274

B. Luo and R. Hancock, Structural graph matching using the em algorithm and singular value decomposition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.10, pp.1120-1136, 2001.

Y. Mansour, M. Mohri, and A. Rostamizadeh, Domain adaptation: Learning bounds and algorithms, Conference on Learning Theory (COLT), pp.19-30, 2009.

S. J. Pan and Q. Yang, A Survey on Transfer Learning, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.10, pp.1345-1359, 2010.
DOI : 10.1109/TKDE.2009.191

V. M. Patel, R. Gopalan, R. Li, and R. Chellappa, Visual domain adaptation: an overview of recent advances, IEEE Signal Processing Magazine, vol.32, issue.3, 2015.

J. Rabin, G. Peyré, J. Delon, and M. Bernot, Wasserstein Barycenter and Its Application to Texture Mixing, Lecture Notes in Computer Science, vol.6667, pp.435-446, 2012.
DOI : 10.1007/978-3-642-24785-9_37

Y. Rubner, C. Tomasi, and L. Guibas, A metric for distributions with applications to image databases, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), pp.59-66, 1998.
DOI : 10.1109/ICCV.1998.710701

K. Saenko, B. Kulis, M. Fritz, and T. Darrell, Adapting Visual Category Models to New Domains, European Conference on Computer Vision (ECCV), ser, pp.213-226, 2010.
DOI : 10.1007/978-3-642-15561-1_16

F. Santambrogio, Optimal transport for applied mathematicians, Birkäuser, vol.87
DOI : 10.1007/978-3-319-20828-2

S. Si, D. Tao, and B. Geng, Bregman Divergence-Based Regularization for Transfer Subspace Learning, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.7, pp.929-942, 2010.
DOI : 10.1109/TKDE.2009.126

J. Solomon, R. Rustamov, G. Leonidas, and A. Butscher, Wasserstein propagation for semi-supervised learning, International Conference on Machine Learning (ICML), pp.306-314, 2014.

M. Sugiyama, S. Nakajima, H. Kashima, P. Buenau, and M. Kawanabe, Direct importance estimation with model selection and its application to covariate shift adaptation, Neural Information Processing Systems (NIPS), 2008.

D. Tuia and G. Camps-valls, Kernel Manifold Alignment for Domain Adaptation, PLOS ONE, vol.31, issue.8, p.148655, 2016.
DOI : 10.1371/journal.pone.0148655.t004

D. Tuia, R. Flamary, A. Rakotomamonjy, and N. Courty, Multitemporal classification without new labels: A solution with optimal transport, 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp), 2015.
DOI : 10.1109/Multi-Temp.2015.7245773

URL : https://hal.archives-ouvertes.fr/hal-01254329

C. Villani, Optimal transport: old and new, ser. Grundlehren der mathematischen Wissenschaften, 2009.
DOI : 10.1007/978-3-540-71050-9

C. Wang, P. Krafft, and S. Mahadevan, Manifold Alignment, Manifold Learning: Theory and Applications, 2011.
DOI : 10.1201/b11431-6

C. Wang and S. Mahadevan, Manifold alignment without correspondence, International Joint Conference on Artificial Intelligence (IJCAI), 2009.

K. Zhang, V. W. Zheng, Q. Wang, J. T. Kwok, Q. Yang et al., Covariate shift in Hilbert space: A solution via surrogate kernels, International Conference on Machine Learning (ICML), 2013.

J. Zheng, M. Liu, R. Chellappa, and P. Phillips, A Grassmann manifold-based domain adaptation approach, International Conference on Pattern Recognition (ICPR), pp.2095-2099, 2012.

. Devis-tuia-boulder, E. Co, and . Lausanne, Since 2014, he is Assistant Professor with the Department of Geography, University of Zurich. He is interested in algorithms for information extraction and data fusion of remote sensing images using machine learning. More info on http://devis.tuia.googlepages.com/ Alain Rakotomamonjy (M'15) is Professor in the Physics department at the University of Rouen since 2006. He obtained his Phd on Signal processing from the university of Orléans in 1997. His recent research activities deal with machine learning and signal processing with applications to brain-computer interfaces and audio applications Alain serves as a regular reviewer for machine learning and signal processing journals