Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
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Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

[1]
N. Brosse.
Around the Langevin Monte Carlo algorithm : extensions and applications, Université Paris Saclay, June 2019.
https://hal.inria.fr/tel-02430579
[2]
B. Karimi.
Non-Convex Optimization for Latent Data Models : Algorithms, Analysis and Applications, Université Paris-Saclay, September 2019.
https://tel.archives-ouvertes.fr/tel-02319140
[3]
G. Robin.
Low-rank methods for heterogeneous and multi-source data, Université Paris-Saclay, June 2019.
https://tel.archives-ouvertes.fr/tel-02168204

Articles in International Peer-Reviewed Journals

[4]
A. Havet, M. Lerasle, É. Moulines.
Density estimation for RWRE, in: Mathematical Methods of Statistics, March 2019, https://arxiv.org/abs/1806.05839. [ DOI : 10.3103/S1066530719010022 ]
https://hal.archives-ouvertes.fr/hal-01815990
[5]
F. Husson, J. Josse, B. Narasimhan, G. Robin.
Imputation of mixed data with multilevel singular value decomposition, in: Journal of Computational and Graphical Statistics, 2019, https://arxiv.org/abs/1804.11087, forthcoming. [ DOI : 10.1080/10618600.2019.1585261 ]
https://hal.archives-ouvertes.fr/hal-01781291
[6]
W. Jiang, J. Josse, M. Lavielle.
Logistic Regression with Missing Covariates – Parameter Estimation, Model Selection and Prediction, in: Computational Statistics and Data Analysis, December 2019, 106907 p, https://arxiv.org/abs/1805.04602, forthcoming. [ DOI : 10.1016/j.csda.2019.106907 ]
https://hal.archives-ouvertes.fr/hal-01958835
[7]
B. Karimi, M. Lavielle, É. Moulines.
f-SAEM: A fast Stochastic Approximation of the EM algorithm for nonlinear mixed effects models, in: Computational Statistics and Data Analysis, July 2019, forthcoming. [ DOI : 10.1016/j.csda.2019.07.001 ]
https://hal.inria.fr/hal-01958248
[8]
L. , M. Berge, A. Tfayli, A. Baillet-Guffroy, P. Prognon, A. Dowek, E. Caudron.
Quantification of gemcitabine intravenous drugs by direct measurement in chemotherapy plastic bags using a handheld Raman spectrometer, in: Talanta, May 2019, vol. 196, pp. 376-380. [ DOI : 10.1016/j.talanta.2018.11.062 ]
https://hal.archives-ouvertes.fr/hal-01970020
[9]
A. Marguet, M. Lavielle, E. Cinquemani.
Inheritance and variability of kinetic gene expression parameters in microbial cells: modeling and inference from lineage tree data, in: Bioinformatics, 2019, vol. 35, no 14, pp. i586-i595. [ DOI : 10.1093/bioinformatics/btz378 ]
https://hal.archives-ouvertes.fr/hal-02317115
[10]
G. Robin, J. Josse, É. Moulines, S. Sardy.
Low-rank model with covariates for count data analysis, in: Journal of Multivariate Analysis, April 2019, vol. 173, https://arxiv.org/abs/1703.02296.
https://hal.archives-ouvertes.fr/hal-01482773
[11]
G. Robin, O. Klopp, J. Josse, É. Moulines, R. Tibshirani.
Main effects and interactions in mixed and incomplete data frames, in: Journal of the American Statistical Association, June 2019. [ DOI : 10.1080/01621459.2019.1623041 ]
https://hal.archives-ouvertes.fr/hal-02423445
[12]
M. Touzot, P. Seris, C. Maheas, J. Vanmassenhove, A.-L. Langlois, K. Moubakir, S. Laplanche, T. Petitclerc, C. Ridel, M. Lavielle.
A mathematical model to predict BNP levels in hemodialysis patients, in: Nephrology, 2019. [ DOI : 10.1111/nep.13586 ]
https://hal.archives-ouvertes.fr/hal-02127228

Invited Conferences

[13]
B. Karimi, B. Miasojedow, É. Moulines, H.-T. Wai.
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme, in: COLT 2019 - 32nd Annual Conference on Conference on Learning Theory, Phoenix, United States, 2019, pp. 1 - 33.
https://hal.inria.fr/hal-02127750

International Conferences with Proceedings

[14]
B. Karimi, M. Lavielle.
Efficient Metropolis-Hastings sampling for nonlinear mixed effects models, in: BAYSM 2018 - Bayesian Young Statisticians Meeting, Warwick, United Kingdom, Bayesian Statistics and New Generations - Proceedings of BAYSM, Springer, 2019.
https://hal.inria.fr/hal-01958247
[15]
B. Karimi, H.-T. Wai, É. Moulines, M. Lavielle.
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods, in: NeurIPS 2019 - 33th Annual Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019.
https://hal.inria.fr/hal-02334656

Conferences without Proceedings

[16]
V. Audigier, F. Husson, J. Josse, M. Resche-Rigon.
Imputation multiple pour données mixtes par analyse factorielle, in: JdS2019 - 51es Journées de Statistique de la Société Française de Statistique, Vandœuvre-lès-Nancy, France, Société Française de Statistique, June 2019.
https://hal-agrocampus-ouest.archives-ouvertes.fr/hal-02355840
[17]
T. Levent, P. Preux, E. Le Pennec, J. Badosa, G. Henri, Y. Bonnassieux.
Energy Management for Microgrids: a Reinforcement Learning Approach, in: ISGT-Europe 2019 - IEEE PES Innovative Smart Grid Technologies Europe, Bucharest, France, IEEE, September 2019, pp. 1-5. [ DOI : 10.1109/ISGTEurope.2019.8905538 ]
https://hal.archives-ouvertes.fr/hal-02382232

Other Publications

[18]
F. Chouly, J. Loubani, A. Lozinski, B. Méjri, K. Merito, S. Passos, A. Pineda.
Computing bi-tangents for transmission belts, January 2020, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02429962
[19]
W. Jiang, M. Bogdan, J. Josse, B. Miasojedow, V. Rockova.
Adaptive Bayesian SLOPE—High-dimensional Model Selection with Missing Values, January 2020, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02430600
[20]
J. Josse, N. Prost, E. Scornet, G. Varoquaux.
On the consistency of supervised learning with missing values, March 2019, https://arxiv.org/abs/1902.06931 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02024202
[21]
B. Karimi, M. Lavielle, É. Moulines.
On the Convergence Properties of the Mini-Batch EM and MCEM Algorithms, October 2019, working paper or preprint.
https://hal.inria.fr/hal-02334485
[22]
A. Sportisse, C. Boyer, J. Josse.
Imputation and low-rank estimation with Missing Non At Random data, January 2019, https://arxiv.org/abs/1812.11409 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-01964720