Bibliography

Articles in International Peer-Reviewed Journals

• 1Y. Atchade, G. Fort, E. Moulines.
  On perturbed proximal gradient algorithms, in: Journal of Machine Learning Research, 2017.
  https://hal.inria.fr/hal-01668239
• 2N. Brosse, A. Durmus, E. Moulines, M. Pereyra.
  Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo, in: Proceedings of Machine Learning Research, 2017, vol. 65, pp. 319-342.
  https://hal.inria.fr/hal-01648665
• 3E. Comets, A. LAVENU, M. Lavielle.
  Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm, in: Journal of Statistical Software, 2017, vol. 80, n^o 3, pp. 1-42. [ DOI : 10.18637/jss.v080.i03 ]
  https://hal.archives-ouvertes.fr/hal-01672496
• 4R. Douc, K. Fokianos, E. Moulines.
  Asymptotic properties of quasi-maximum likelihood estimators in observation-driven time series models, in: Electronic journal of statistics , 2017, vol. 11, n^o 2, pp. 2707 - 2740. [ DOI : 10.1214/17-EJS1299 ]
  https://hal.inria.fr/hal-01668243
• 5A. Durmus, E. Moulines.
  Nonasymptotic convergence analysis for the unadjusted Langevin algorithm, in: The Annals of Applied Probability : an official journal of the institute of mathematical statistics, June 2017, vol. 27, n^o 3, pp. 1551 - 1587. [ DOI : 10.1214/16-AAP1238 ]
  https://hal.inria.fr/hal-01668245
• 6M. Lavielle.
  Pharmacometrics Models with Hidden Markovian Dynamics, in: Journal of Pharmacokinetics and Pharmacodynamics, 2017, pp. 1-15. [ DOI : 10.1007/s10928-017-9541-1 ]
  https://hal.inria.fr/hal-01665722
• 7F. MAIRE, E. Moulines, S. Lefebvre.
  Online EM for functional data, in: Computational Statistics and Data Analysis, July 2017, vol. 111, pp. 27 - 47. [ DOI : 10.1016/j.csda.2017.01.006 ]
  https://hal.inria.fr/hal-01668241
• 8N. M. Nguyen, S. Le Corff, E. Moulines.
  Particle rejuvenation of Rao-Blackwellized sequential Monte Carlo smoothers for conditionally linear and Gaussian models, in: EURASIP Journal on Advances in Signal Processing, December 2017, vol. 2017:54, pp. 1-15. [ DOI : 10.1186/s13634-017-0489-5 ]
  https://hal.inria.fr/hal-01668374
• 9H.-T. Wai, J. Lafond, A. Scaglione, E. Moulines.
  Decentralized Frank–Wolfe Algorithm for Convex and Nonconvex Problems, in: IEEE Transactions on Automatic Control, November 2017, vol. 62, n^o 11, pp. 5522 - 5537. [ DOI : 10.1109/TAC.2017.2685559 ]
  https://hal.inria.fr/hal-01668247

International Conferences with Proceedings

• 10H.-T. Wai, J. Lafond, A. Scaglione, E. Moulines.
  Fast and privacy preserving distributed low-rank regression, in: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, United States, 2017.
  https://hal.inria.fr/hal-01668252

Other Publications

• 11N. Brosse, A. Durmus, E. Moulines.
  Normalizing constants of log-concave densities, November 2017, working paper or preprint.
  https://hal.inria.fr/hal-01648666
• 12N. Brosse, A. Durmus, E. Moulines, S. Sabanis.
  The Tamed Unadjusted Langevin Algorithm, November 2017, working paper or preprint.
  https://hal.inria.fr/hal-01648667
• 13E. Derman, E. Le Pennec.
  Clustering and Model Selection via Penalized Likelihood for Different-sized Categorical Data Vectors, September 2017, https://arxiv.org/abs/1709.02294 - working paper or preprint.
  https://hal.inria.fr/hal-01583692
• 14E. Gautier, E. Le Pennec.
  Adaptive estimation in the nonparametric random coefficients binary choice model by needlet thresholding, November 2017, https://arxiv.org/abs/1106.3503 - working paper or preprint.
  https://hal.inria.fr/inria-00601274
• 15G. Robin, J. Josse, E. Moulines, S. Sardy.
  Low-rank Interaction Contingency Tables, September 2017, https://arxiv.org/abs/1703.02296 - working paper or preprint.
  https://hal.archives-ouvertes.fr/hal-01482773