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Publications and preprints


My publications can be found in   Google Scholar,   Arxiv,    MathScinet

Publications by year


[1] E. Del Barrio and J.-M. Loubes. Central limit theorems for empirical transportation
cost in general dimension. Annals of Probability arXiv preprint arXiv :1705.01299,
to appear 2018
[2] F. Bachoc, F. Gamboa, J-M. Loubes, N. Venet. Gaussian Process Regression Model
for Distribution Inputs IEEE Trans. Inform. Theory, to appear, 2018.
[3] E. Del Barrio, P. Gordaliza, H. Lescornel, and J.-M. Loubes. A statistical analysis of a
deformation model with wasserstein barycenters : estimation procedure and goodness
of t test. Journal of Multivariate Analysis, to appear 2018.
[4] C. Barreyre, B. Laurent, J.-M. Loubes, B. Cabon, and L. Boussouf. Multiple testing
for outlier detection in functional data. Space Ops bests student paper award, 2018.
[5] T. Le Gouic and J.-M. Loubes. Existence and consistency of wasserstein barycenters.
Probability Theory and Related Fields, 168(3-4) :901{917, 2017.
[6] D. Velandia, F. Bachoc, M. Bevilacqua, X. Gendre, J.-M. Loubes, Maximum likelihood
estimation for a bivariate gaussian process under xed domain asymptotics.
Electronic Journal of Statistics, 11(2) :2978{3007, 2017.
[7] J.-M. Loubes, B. Pelletier, Prediction by quantization of a conditional distribution.
Electronic journal of statistics, 11(1) :2679{2706, 2017.
[8] E. Del Barrio, J.-M. Loubes, and B. Pelletier. An inverse problem : Recovery of a distribution
using wasserstein barycenter. Annals of Economics and Statistics/Annales
d’ Economie et de Statistique, (128) :229{259, 2017.
[9] P. C. Besse, B. Guillouet, J.-M. Loubes, and F. Royer. Destination prediction by
trajectory distribution based model. IEEE Transactions on Intelligent Transportation
Systems, 2017.
[10] S. Da Veiga, J.-M. Loubes, and M. Sols. Ecient estimation of conditional covariance
matrices for dimension reduction. Communications in Statistics-Theory and Methods,
46(9) :4403{4424, 2017.
[11] J. Braga, P. Bouvier, J. R. Dherbey, P. Balaresque, L. Risser, J.-M. Loubes, J. Dumoncel,
B. Duployer, and C. Tenailleau. Echoes from the past : New insights into
the early hominin cochlea from a phylo-morphometric approach. Comptes Rendus
Palevol, 2017.

[12] T. Espinasse and J.-M. Loubes. A kriging procedure for processes indexed by graphs.
Stat. Inference Stoch. Process., 19(2) :159{173, 2016.
[13] P. C. Besse, B. Guillouet, J. M. Loubes, and F. Royer. Review and perspective for
distance-based clustering of vehicle trajectories. IEEE Transactions on Intelligent
Transportation Systems, PP(99) :1{12, 2016.


[14] E. Boissard, T. Le Gouic, and J.-M. Loubes. Distribution’s template estimate with
Wasserstein metrics. Bernoulli, 21(2) :740{759, 2015.
[15] M. Agullo-Antoln, J. A. Cuesta-Albertos, H. Lescornel, and J.-M. Loubes. A parametric
registration model for warped distributions with Wasserstein’s distance. J.
Multivariate Anal., 135 :117{130, 2015.
[16] P. Fraysse, H. Lescornel, and J.-M. Loubes. A Robbins Monro procedure for the estimation
of parametric deformations on random variables. Statist. Sinica, 25(2) :631{
654, 2015.
[17] S. Gallon, F. Gamboa, and J. M. Loubes. Functional calibration estimation by the
maximum entropy on the mean principle. Statistics, 49(5) :989{1004, 2015.
[18] J Braga, JM Loubes. Disproportionate cochlear length in genus homo shows a high
phylogenetic signal during apes ? hearing evolution. PloS one, 10(6) :e0127780, 2015.


[19] T. Espinasse, F. Gamboa, and J.-M. Loubes. Parametric estimation for Gaussian
elds indexed by graphs. Probab. Theory Related Fields, 159(1-2) :117{155, 2014.
[20] J. M. Loubes and C. Marteau. Goodness-of-t testing strategies from indirect observations.
J. Nonparametr. Stat., 26(1) :85{99, 2014.
[21] M. Blazere, J.-M. Loubes, and F. Gamboa. Oracle inequalities for a group lasso
procedure applied to generalized linear models in high dimension. IEEE Trans.
Inform. Theory, 60(4) :2303{2318, 2014.
[22] C. Dimeglio, S. Gallon, J.-M. Loubes, and E. Maza. A robust algorithm for template
curve estimation based on manifold embedding. Comput. Statist. Data Anal., 70 :373{
386, 2014.
[23] R. Biscay Lirio, D. G. Camejo, J.-M. Loubes, and L. Mu~niz Alvarez. Estimation
of covariance functions by a fully data-driven model selection procedure and its application
to Kriging spatial interpolation of real rainfall data. Stat. Methods Appl.,
23(2) :149{174, 2014.
[24] H. Lescornel, J.-M. Loubes, and C. Chabriac. Unbiased risk estimation method for
covariance estimation. ESAIM Probab. Stat., 18 :251{264, 2014.


[25] S. Cohen, F. Gamboa, C. Lacaux, and J.-M. Loubes. LAN property for some fractional
type Brownian motion. ALEA Lat. Am. J. Probab. Math. Stat., 10(1) :91{106,
[26] S. Gallon, J.-M. Loubes, and E. Maza. Statistical properties of the quantile normalization
method for density curve alignment. Math. Biosci., 242(2) :129{142, 2013.
[27] J.-M. Loubes and A.-F. Yao. Kernel inverse regression for random elds. Int. J.
Appl. Math. Stat., 32(2) :1{26, 2013.
[28] J. Braga, J. F. Thackeray, J. Dumoncel, D. Descouens, L. Bruxelles, J.-M. Loubes,
J.-L. Kahn, M. Stampanoni, L. Bam, J. Homan, et al. A new partial temporal bone
of a juvenile hominin from the site of kromdraai b (south africa). Journal of human
evolution, 65(4) :447{456, 2013.


[29] J. Bigot, J.-M. Loubes, and M. Vimond. Semiparametric estimation of shifts on
compact Lie groups for image registration. Probab. Theory Related Fields, 152(3-
4) :425{473, 2012.
[30] B. Laurent, J.-M. Loubes, and C. Marteau. Non asymptotic minimax rates of testing
in signal detection with heterogeneous variances. Electron. J. Stat., 6 :91{122, 2012.
[31] J.-M. Loubes and C. Marteau. Adaptive estimation for an inverse regression model
with unknown operator. Stat. Risk Model., 29(3) :215{242, 2012.
[32] J.-M. Loubes and P. Rochet. Approximate maximum entropy on the mean for instrumental
variable regression. Statist. Probab. Lett., 82(5) :972{978, 2012.
[33] R. Biscay, H. Lescornel, and J.-M. Loubes. Adaptive covariance estimation with
model selection. Math. Methods Statist., 21(4) :283{297, 2012.

[34] J. Bigot, R. J. Biscay, J.-M. Loubes, and L. Mu~niz-Alvarez. Group lasso estimation
of high-dimensional covariance matrices. J. Mach. Learn. Res., 12 :3187{3225, 2011.
[35] B. Laurent, J.-M. Loubes, and C. Marteau. Testing inverse problems : a direct or an
indirect problem ? J. Statist. Plann. Inference, 141(5) :1849{1861, 2011.
[36] F. Gamboa, J.-M. Loubes, and P. Rochet. Maximum entropy estimation for survey
sampling. J. Statist. Plann. Inference, 141(1) :305{317, 2011.
[37] J.-F. Dupuy, J.-M. Loubes, and E. Maza. Non parametric estimation of the structural
expectation of a stochastic increasing function. Stat. Comput., 21(1) :121{136, 2011.
[38] T. Espinasse, F. Gamboa, and J.-M. Loubes. Estimation error for blind Gaussian
time series prediction. Math. Methods Statist., 20(3) :206{223, 2011.
[39] J.-M. Loubes and D. Paindaveine. Local asymptotic normality property for lacunar
wavelet series multifractal model. ESAIM Probab. Stat., 15 :69{82, 2011.
[40] J.-M. Loubes and C. Marteau. Paths towards adaptive estimation for instrumental
variable regression. Publ. Mat. Urug., 12 :99{122, 2011.
[41] J. Bigot, R. Biscay, J.-M. Loubes, and L. Mu~niz-Alvarez. Nonparametric estimation
of covariance functions by model selection. Electron. J. Stat., 4 :822{855, 2010.
[42] F. Autin, E. Le Pennec, J. Loubes, and V. Rivoirard. Maxisets for model selection.
Constr. Approx., 31(2) :195{229, 2010.
[43] J.-M. Loubes and C. Lude~na. Penalized estimators for non linear inverse problems.
ESAIM Probab. Stat., 14 :173{191, 2010.
[44] J. Bigot, S. Gadat, and J.-M. Loubes. Statistical M-estimation and consistency in
large deformable models for image warping. J. Math. Imaging Vision, 34(3) :270{290,
[45] I. Castillo and J.-M. Loubes. Estimation of the distribution of random shifts deformation.
Math. Methods Statist., 18(1) :21{42, 2009.
[46] J.-M. Loubes and Y. Yan. Penalized maximum likelihood estimation with l1 penalty.
Int. J. Appl. Math. Stat., 14(J09) :35{46, 2009.
[47] J.-M. Loubes and V. Rivoirard. Review of rates of convergence and regularity conditions
for inverse problems. Int. J. Tomogr. Stat., 11(S09) :61{82, 2009.
[48] J. Treil, J. Braga, J.-M. Loubes, E. Maza, J.-M. Inglese, J. Casteigt, and B. Waysenson.
3d tooth modeling for orthodontic assessment. Seminars in Orthodontics,
15(1) :42{47, 2009.


[49] J.-M. Loubes and C. Lude~na. Adaptive complexity regularization for linear inverse
problems. Electron. J. Stat., 2 :661{677, 2008.
[50] J.-M. Loubes and B. Pelletier. Maximum entropy solution to ill-posed inverse problems
with approximately known operator. J. Math. Anal. Appl., 344(1) :260{273,
[51] J.-M. Loubes and B. Pelletier. A kernel-based classier on a Riemannian manifold.
Statist. Decisions, 26(1) :35{51, 2008.
[52] J.-M. Loubes. l1 penalty for ill-posed inverse problems. Comm. Statist. Theory
Methods, 37(8-10) :1399{1411, 2008.
[53] F. Gamboa, J.-M. Loubes, and E. Maza. Semi-parametric estimation of shifts. Elec-
tron. J. Stat., 1 :616{640, 2007.
[54] F. Gamboa and J.-M. Loubes. Estimation of parameters of a multifractal process.
TEST, 16(2) :383{407, 2007.
[55] C. Lacaux and J.-M. Loubes. Hurst exponent estimation of fractional Levy motion.
ALEA Lat. Am. J. Probab. Math. Stat., 3 :143{164, 2007.

[56] J.-M. Loubes, E. Maza, M. Lavielle, and L. Rodrguez. Road tracking description
and short term travel time forecasting, with a classication method. Canad. J.
Statist., 34(3) :475{491, 2006.
[57] A. K. Fermn, J.-M. Loubes, and C. Lude~na. Bayesian methods for a particular
inverse problem : seismic tomography. Int. J. Tomogr. Stat., 4(W06) :1{19, 2006.
[58] D. Chafa and J.-M. Loubes. On nonparametric maximum likelihood for a class of
stochastic inverse problems. Statist. Probab. Lett., 76(12) :1225{1237, 2006.

[59] F. Gamboa and J.-M. Loubes. Wavelet estimation of a multifractal function. Ber-
noulli, 11(2) :221{246, 2005.
2004 :
[60] J.-M. Loubes and P. Massart. Discussion of least angle regression by efron et al.
Annals of Statistics, 32(2) :475{481, 2004.
2002 :
[61] J.-M. Loubes and S. van de Geer. Adaptive estimation with soft thresholding penalties.
Statist. Neerlandica, 56(4) :454{479, 2002.
Livres et Conferences
[62] T. Le Gouic and J.-M. Loubes. Barycenter in wasserstein spaces : Existence and
consistency. In International Conference on Networked Geometric Science of Infor-
mation, pages 104{108. Springer International Publishing, 2015.
[63] J.-M. Loubes and P. Rochet. Regularization with approximated L2 maximum entropy
method. In Mathematical methods for signal and image analysis and representation,
volume 41 of Comput. Imaging Vision, pages 275{290. Springer, London, 2012.
[64] G. Allain, F. Gamboa, P. Goudal, J.-M. Loubes, and E. Maza. A statistical framework
for road trac prediction. 16th ITS World Congress and Exhibition on Intelligent
Transport Systems and Services, 2009.
[65] J. Bigot, J.-M. Loubes, M. Vimond, et al. Semiparametric estimation of rigid transformations
on compact lie groups. In 2nd MICCAI Workshop on Mathematical Foun-
dations of Computational Anatomy, pages 92{104, 2008.
[66] J.-M. Loubes. `1 sparsity and applications in estimation. C. R. Math. Acad. Sci.
Paris, 344(6) :399{402, 2007.

Brevets  PATENTS
Procede de prediction de la vitesse d’un conducteur au volant d’un vehicule, Dec. 12
2016. WO Patent App. PCT/FR2016/0970,037.
[68] A. BRUNET, J. LOUBES, J. AZAIS, and M. Courtney. Method of identication
of a relationship between biological elements, Dec. 3 2015. WO Patent App.
[69] J. LOUBES, F. GAMBOA, J. KIEN, and G. ALLAIN. Methode d’estimation d’un
temps de parcours d’un vehicule dans un reseau routier, Dec. 27 2013. WO Patent
App. PCT/FR2013/051,423.

Travaux Soumis
[70] T. Epelbaum, F. Gamboa, J.-M. Loubes, and J. Martin. Deep learning applied to
road trac speed forecasting. arXiv preprint arXiv :1710.08266, 2018.
[71] M. Antolin, E. Del Barrio, and J.-M. Loubes. A data driven trimming procedure for
robust classication. arXiv preprint arXiv :1701.05065, 2017.
[72] J.-M. Loubes, C. Marteau, and M. Sols. Rates of convergence in conditional covariance
matrix estimation. arXiv preprint arXiv :1310.8244, 2018.
[73] M. Blazere, F. Gamboa, and J.-M. Loubes. A unied framework for the study of the
pls estimator’s properties. arXiv preprint arXiv :1411.0229, 2017.
[74] Philippe Besse, Eustasio Del Barrio, Paula Gordaliza, and Jean-Michel Loubes.
Condence intervals for testing disparate impact in fair learning. ArXiv e-prints
1807.06362, 2018.
[75] F. Gamboa, , Eustasio Del Barrio, Paula Gordaliza, and Jean-Michel Loubes. Obtaining
fairness using optimal transport theory
[76] Eustasio Del Barrio, Paula Gordaliza, and Jean-Michel Loubes. A Central Limit
Theorem for Lp transportation cost with applications to Fairness Assessment in Machine


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