M. Arcones and E. Giné, U-processes indexed by VapnikChervonenkis classes of functions with applications to asymptotics and bootstrap of U-statistics with estimated parameters, Stochastic Processes and their Applications, vol.52, pp.17-38, 1994.

B. Babenko, S. Branson, and S. J. Belongie, Similarity metrics for categorization: From monolithic to category specific, ICCV, 2009.

A. Bellet and A. Habrard, Robustness and Generalization for Metric Learning, Neurocomputing, vol.151, issue.1, pp.259-267, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01075370

A. Bellet, A. Habrard, and M. Sebban, Similarity Learning for Provably Accurate Sparse Linear Classification, ICML, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00708401

A. Bellet, A. Habrard, M. M. Sebban, and . Learning, , 2015.

A. Bendale and T. E. Boult, Towards Open World Recognition, CVPR, 2015.
DOI : 10.1109/cvpr.2015.7298799
URL : http://arxiv.org/pdf/1412.5687

G. Blom, Some properties of incomplete U-statistics, Biometrika, vol.63, issue.3, pp.573-580, 1976.

S. Boucheron, O. Bousquet, and G. Lugosi, Theory of classification : a survey of some recent advances, ESAIM: Probability and Statistics, vol.9, pp.323-375, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00017923

O. Bousquet, S. Boucheron, and G. Lugosi, Introduction to statistical learning theory, Advanced Lectures on Machine Learning, pp.169-207, 2004.
DOI : 10.1007/978-3-540-28650-9_8

Q. Cao, Z. Guo, Y. , and Y. , Generalization Bounds for Metric and Similarity Learning, Machine Learning, vol.102, pp.115-132, 2016.
DOI : 10.1007/s10994-015-5499-7
URL : https://link.springer.com/content/pdf/10.1007%2Fs10994-015-5499-7.pdf

G. Chechik, V. Sharma, U. Shalit, and S. Bengio, Large Scale Online Learning of Image Similarity Through Ranking, Journal of Machine Learning Research, vol.11, pp.1109-1135, 2010.

S. Clémençon and N. Vayatis, Ranking the best instances, Journal of Machine Learning Research, vol.8, pp.2671-2699, 2007.

S. Clémençon and N. Vayatis, Tree-based ranking methods, IEEE Transactions on Information Theory, vol.55, issue.9, pp.4316-4336, 2009.

S. Clémençon, I. Colin, and A. Bellet, Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics, Journal of Machine Learning Research, vol.17, issue.76, pp.1-36, 2016.

S. Clémençon and N. Vayatis, Overlaying classifiers: A practical approach to optimal scoring, Constructive Approximation, vol.32, issue.3, pp.619-648, 2010.

S. Clémençon, G. Lugosi, and N. Vayatis, Ranking and Empirical Minimization of U-Statistics, The Annals of Statistics, vol.36, issue.2, pp.844-874, 2008.

F. Cucker and S. Smale, Best choices for regularization parameters in learning theory: On the bias-variance problem, vol.2, pp.413-428, 2002.

V. De-la-peña and E. Giné, Decoupling: From dependence to independence. Probability and its Applications, 1999.

R. M. Dudley, Uniform Central Limit Theorems, 1999.
DOI : 10.1017/cbo9781139014830

J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov, Neighbourhood Components Analysis, NIPS, 2004.

M. Guillaumin, J. Verbeek, and C. Schmid, Is that you? Metric Learning Approaches for Face Identification, CVPR, 2009.
DOI : 10.1109/iccv.2009.5459197
URL : https://hal.archives-ouvertes.fr/inria-00439290

W. Hoeffding, A class of statistics with asymptotically normal distribution, The Annals of Mathematical Statistics, vol.19, pp.293-325, 1948.

J. Huo, Y. Gao, Y. Shi, Y. , and H. , Cross-modal metric learning for auc optimization, IEEE Transactions on Neural Networks and Learning Systems, issue.99, pp.1-13, 2018.

A. Jain, L. Hong, P. , and S. , Biometric identification, Communications of the ACM, vol.43, issue.2, pp.90-98, 2000.

A. K. Jain, A. Ross, P. , and S. , An introduction to biometric recognition, IEEE Transactions on Circuits and Systems for Video Technology, vol.14, pp.4-20, 2004.
DOI : 10.1109/tcsvt.2003.818349
URL : http://biometrics.cse.msu.edu/Publications/GeneralBiometrics/JainRossPrabhakar_BiometricIntro_CSVT04.pdf

A. K. Jain, A. A. Ross, and K. Nandakumar, Biometrics, 2011.

L. Jain, B. Mason, and R. Nowak, Learning LowDimensional Metrics, NIPS, 2017.

R. Jin, S. Wang, and Y. Zhou, Regularized Distance Metric Learning: Theory and Algorithm, NIPS, 2009.

B. Kulis, Metric Learning: A Survey. Foundations and Trends in Machine Learning, vol.5, pp.287-364, 2012.