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Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

Articles in International Peer-Reviewed Journals

  • 2J.-B. Barbry, A.-S. Poinsard, T. Bastogne, O. Balland.

    Short-term effects of ocular 2% dorzolamide, 0.5% timolol or 0.005% latanoprost on the anterior segment architecture in healthy cats: a prospective study, in: Open Veterinary Journal, 2020, forthcoming.

    https://hal.archives-ouvertes.fr/hal-02396549
  • 3M. Ben Abdallah, M. Blonski, S. Wantz-Mézières, Y. Gaudeau, L. Taillandier, J.-M. Moureaux, A. Darlix, N. Menjot De Champfleur, H. Duffau.

    Data-driven predictive models of diffuse low-grade gliomas under chemotherapy, in: IEEE Journal of Biomedical and Health Informatics, January 2019, vol. 23, no 1, pp. 38-46. [ DOI : 10.1109/JBHI.2018.2834159 ]

    https://hal.archives-ouvertes.fr/hal-02097695
  • 4A. Gégout-Petit, L. Guérin-Dubrana, S. Li.

    A new centered spatio-temporal autologisticregression model with an application to local spread of plant diseases, in: Spatial Statistics, May 2019, no 31, https://arxiv.org/abs/1811.06782.

    https://hal.inria.fr/hal-01926115
  • 5A. Lejay, L. Lenôtre, G. Pichot.

    An exponential timestepping algorithm for diffusion with discontinuous coefficients, in: Journal of Computational Physics, November 2019, vol. 396, pp. 888-904. [ DOI : 10.1016/j.jcp.2019.07.013 ]

    https://hal.inria.fr/hal-01806465
  • 6F. Rech, G. Herbet, Y. Gaudeau, S. Wantz-Mézières, J.-M. Moureaux, S. Moritz-Gasser, H. Duffau.

    A probabilistic map of negative motor areas of the upper limb and face: a brain stimulation study, in: Brain - A Journal of Neurology , April 2019, vol. 142, no 4, pp. 952-965. [ DOI : 10.1093/brain/awz021 ]

    https://hal.archives-ouvertes.fr/hal-02314548
  • 7A. Taccoen, C. C. Piedallu, I. Seynave, V. V. Perez, A. Gégout-Petit, L.-M. Nageleisen, J.-D. Bontemps, J.-C. Gégout.

    Background mortality drivers of European tree species: climate change matters, in: Proceedings of the Royal Society B: Biological Sciences, April 2019, vol. 286, no 1900, 20190386 p. [ DOI : 10.1098/rspb.2019.0386 ]

    https://hal.archives-ouvertes.fr/hal-02095574
  • 8S. Toupance, D. Villemonais, D. Germain, A. Gégout-Petit, E. Albuisson, A. Benetos.

    The individual’s signature of telomere length distribution, in: Scientific Reports, January 2019, vol. 9, no 1, 8 p. [ DOI : 10.1038/s41598-018-36756-8 ]

    https://hal.inria.fr/hal-01925000

International Conferences with Proceedings

  • 10M. Geist, B. Scherrer, O. Pietquin.

    A Theory of Regularized Markov Decision Processes, in: ICML 2019 - Thirty-sixth International Conference on Machine Learning, Long Island, United States, June 2019, https://arxiv.org/abs/1901.11275 - ICML 2019.

    https://hal.inria.fr/hal-02273741
  • 11N. Sahki, A. Gégout-Petit, S. Wantz-Mézières.

    Détection Statistique de Rupture dans le Cadre Online, in: JdS 2019 - 51èmes Journées de Statistique, Nancy, France, June 2019.

    https://hal.inria.fr/hal-02289680

Conferences without Proceedings

  • 12B. Bastien, A. Gégout-Petit, A. Muller-Gueudin.

    Aggregation of statistical methods for the selection of correlated and high dimensional variables, in: Séminaire AgroParisTech, Paris, France, May 2019.

    https://hal.archives-ouvertes.fr/hal-02360968
  • 13J. Deleforterie, Y. Kolasa, L. Batista, J. Hutin, T. Bastogne.

    Quality-by-Design for the safe development of medical devices containing nanomaterials. A study case in photodynamic therapy, in: NanoMed Europe, NME 2019, Braga, Portugal, June 2019, Présentation Poster.

    https://hal.archives-ouvertes.fr/hal-02396520
  • 14F. Greciet, R. Azaïs, A. Gégout-Petit.

    Régression polynomiale par morceaux sous contrainte de régularité pour la propagation de fissures, in: JdS 2019 - 51èmes Journées de Statistique, Nancy, France, June 2019, pp. 1-5.

    https://hal.archives-ouvertes.fr/hal-02172747
  • 15A. Gégout-Petit, A. Gueudin, C. Karmann.

    Network inference for truncated gaussian data, in: European Meeting of Statisticians, Palermo, Italy, July 2019.

    https://hal.archives-ouvertes.fr/hal-02369239
  • 16C. Karmann, A. Gégout-Petit, A. Muller-Gueudin.

    Inférence de réseaux pour des données gaussiennes inflatées en zéros par double troncature, in: Journées de statistique 2019, Nancy, France, June 2019.

    https://hal.archives-ouvertes.fr/hal-02335105
  • 17C. Karmann, A. Gégout-Petit, A. Muller-Gueudin.

    Méthode des knockoffs revisités pour la sélection de variables. Application à l'inférence de réseaux pour modèles inflatés en zéro, in: Journées NETBIO Saclay, Saclay, France, October 2019.

    https://hal.archives-ouvertes.fr/hal-02354748
  • 18C. Karmann, A. Gégout-Petit, A. Muller-Gueudin.

    Penalized ordinal logistic regression using cumulative logits, in: Journée scientifique FCH : "Méthodes et modèles pour comprendre les réseaux biologiques", Nancy, France, 2019.

    https://hal.archives-ouvertes.fr/hal-02354731
  • 19B. Lalloué, J.-M. Monnez, E. Albuisson.

    Actualisation en ligne d'un score d'ensemble, in: 51e Journées de Statistique, Nancy, France, Société Française de Statistique, June 2019.

    https://hal.archives-ouvertes.fr/hal-02152352
  • 20B. Lalloué, J.-M. Monnez, E. Albuisson.

    Streaming constrained binary logistic regression with online standardized data, in: SFC 2019 - 26émes Rencontres de la Société Francophone de Classification, Nancy, France, September 2019.

    https://hal.archives-ouvertes.fr/hal-02278090
  • 21J.-M. Monnez.

    Convergence du processus de Oja et ACP en ligne, in: 51èmes Journées de Statistique, Nancy, 2019, NANCY, France, Efoevi Koudou, June 2019.

    https://hal.archives-ouvertes.fr/hal-02383570
  • 22A. Muller-Gueudin, A. Debussche, A. Crudu.

    Modeling of gene regulation networks by deterministic processes by pieces, in: Journée de la Fédération Charles Hermite, Vandoeuvre-les-Nancy, France, June 2019, pp. 1-37.

    https://hal.archives-ouvertes.fr/hal-02360992
  • 23A. Muller-Gueudin, A. Gégout-Petit.

    Aggregation of statistical methods for the selection of correlated variables, in large dimension, in: JdS 2019 - 51emes Journées de Statistique de la SFDS, Vandoeuvre-les-Nancy, France, June 2019.

    https://hal.archives-ouvertes.fr/hal-02360974
  • 24R. Postoyan, M. Granzotto, L. Buşoniu, B. Scherrer, D. Nešić, J. Daafouz.

    Stability guarantees for nonlinear discrete-time systems controlled by approximate value iteration, in: 58th IEEE Conference on Decision and Control, CDC 2019, Nice, France, December 2019, Version longue de l'article du même titre et des mêmes auteurs des proceedings de l'IEEE Conference on Decision on Control 2019, Nice, France.

    https://hal.archives-ouvertes.fr/hal-02271268
  • 25V. Roulette, G. Delplanque, J. Deleforterie, T. Bastogne.

    A contribution in nanoinformatics to facilitate the collection of structured data for Quality-by-Design in nanomedicine, in: NanoMed Europe, NME 2019, Braga, Portugal, June 2019.

    https://hal.archives-ouvertes.fr/hal-02396540
  • 26N. Sahki, A. Gégout-Petit, S. Wantz-Mézières.

    New Detection Thresholds and Stop Rules for CUSUM Online Detection, in: ENBIS 2019 - 19th Annual Conference of the European Network for Business and Industrial Statistics, Budapest, Hungary, September 2019.

    https://hal.archives-ouvertes.fr/hal-02289501

Other Publications

  • 27B. Bastien, T. Boukhobza, H. Dumond, A. Gégout-Petit, A. Muller-Gueudin, C. Thiébaut.

    A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology, September 2019, https://arxiv.org/abs/1909.05481 - working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-02173568
  • 28S. Ferrigno, M. Maumy-Bertrand.

    Estimation of reference curves for fetal weight, December 2019, CMStatistics 2019, Poster.

    https://hal.inria.fr/hal-02389157
  • 29A. Gégout-Petit, A. Muller-Gueudin, C. Karmann.

    Graph estimation for Gaussian data zero-inflated by double truncation, November 2019, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-02367344
  • 30A. Gégout-Petit, A. Muller-Gueudin, C. Karmann.

    The revisited knockoffs method for variable selection in L1-penalised regressions, November 2019, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-01799914
  • 31B. Lalloué, J.-M. Monnez, E. Albuisson.

    Streaming constrained binary logistic regression with online standardized data. Application to scoring heart failure, June 2019, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-02156324
  • 32J.-M. Monnez, A. Skiredj.

    Convergence of a normed eigenvector stochastic approximation process and application to online principal component analysis of a data stream, May 2019, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-01844419
  • 33A. Muller-Gueudin, A. Gégout-Petit.

    Package 'armada' : A Statistical Methodology to Select Covariates in High-Dimensional Data under Dependence, April 2019, An R package, available on the CRAN. A Statistical Methodology to Select Covariates in High-Dimensional Data under Dependence.

    https://hal.archives-ouvertes.fr/hal-02363338
  • 34N. Sahki, A. Gégout-Petit, S. Wantz-Mézières.

    Change-point detection method for the prediction of dreaded events during online monitoring of lung transplant patients, December 2019, Annual PhD students conference IAEM Lorraine, APIL 2019, Poster.

    https://hal.inria.fr/hal-02392756
  • 35N. Sahki, A. Gégout-Petit, S. Wantz-Mézières.

    Performance Study of Detection Thresholds for CUSUM statistic in a Sequential Context, December 2019, working paper or preprint.

    https://hal.inria.fr/hal-02389331
References in notes
  • 36R. Azaïs.

    A recursive nonparametric estimator for the transition kernel of a piecewise-deterministic Markov process, in: ESAIM: Probability and Statistics, 2014, vol. 18, pp. 726–749.
  • 37R. Azaïs, F. Dufour, A. Gégout-Petit.

    Nonparametric estimation of the jump rate for non-homogeneous marked renewal processes, in: Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, Institut Henri Poincaré, 2013, vol. 49, no 4, pp. 1204–1231.
  • 38R. Azaïs, F. Dufour, A. Gégout-Petit.

    Non-Parametric Estimation of the Conditional Distribution of the Interjumping Times for Piecewise-Deterministic Markov Processes, in: Scandinavian Journal of Statistics, December 2014, vol. 41, no 4, pp. 950–969. [ DOI : 10.1111/sjos.12076 ]

    https://hal.archives-ouvertes.fr/hal-01103700
  • 39R. Azaïs, A. Muller-Gueudin.

    Optimal choice among a class of nonparametric estimators of the jump rate for piecewise-deterministic Markov processes, in: Electronic journal of statistics , 2016.

    https://hal.archives-ouvertes.fr/hal-01168651
  • 40J. M. Bardet, G. Lang, G. Oppenheim, A. Philippe, S. Stoev, M. Taqqu.

    Semi-parametric estimation of the long-range dependence parameter: a survey, in: Theory and applications of long-range dependence, Birkhauser Boston, 2003, pp. 557-577.
  • 41T. Bastogne, S. Mézières-Wantz, N. Ramdani, P. Vallois, M. Barberi-Heyob.

    Identification of pharmacokinetics models in the presence of timing noise, in: Eur. J. Control, 2008, vol. 14, no 2, pp. 149–157.

    http://dx.doi.org/10.3166/ejc.14.149-157
  • 42T. Bastogne, A. Samson, P. Vallois, S. Wantz-Mézières, S. Pinel, D. Bechet, M. Barberi-Heyob.

    Phenomenological modeling of tumor diameter growth based on a mixed effects model, in: Journal of theoretical biology, 2010, vol. 262, no 3, pp. 544–552.
  • 43D. Bertsekas, J. Tsitsiklis.

    Neurodynamic Programming, Athena Scientific, 1996.
  • 44H. Biermé, C. Lacaux, H.-P. Scheffler.

    Multi-operator Scaling Random Fields, in: Stochastic Processes and their Applications, 2011, vol. 121, no 11, pp. 2642-2677, MAP5 2011-01. [ DOI : 10.1016/j.spa.2011.07.002 ]

    http://hal.archives-ouvertes.fr/hal-00551707/en/
  • 45H. Cardot, P. Cénac, J.-M. Monnez.

    A fast and recursive algorithm for clustering large datasets with k-medians, in: Computational Statistics & Data Analysis, 2012, vol. 56, no 6, pp. 1434–1449.
  • 46J. F. Coeurjolly.

    Simulation and identification of the fractional brownian motion: a bibliographical and comparative study, in: Journal of Statistical Software, 2000, vol. 5, pp. 1–53.
  • 47M. H. Davis.

    Piecewise-deterministic Markov processes: A general class of non-diffusion stochastic models, in: Journal of the Royal Statistical Society. Series B (Methodological), 1984, pp. 353–388.
  • 48A. Deya, S. Tindel.

    Rough Volterra equations. I. The algebraic integration setting, in: Stoch. Dyn., 2009, vol. 9, no 3, pp. 437–477.

    http://dx.doi.org/10.1142/S0219493709002737
  • 49M. Doumic, M. Hoffmann, N. Krell, L. Robert.

    Statistical estimation of a growth-fragmentation model observed on a genealogical tree, in: Bernoulli, 2015, vol. 21, no 3, pp. 1760–1799.
  • 50S. Ferrigno, G. R. Ducharme.

    Un test d'adéquation global pour la fonction de répartition conditionnelle, in: Comptes Rendus Mathematique, 2005, vol. 341, no 5, pp. 313–316.
  • 51S. Ferrigno, M. Maumy-Bertrand, A. Muller-Gueudin.

    Uniform law of the logarithm for the local linear estimator of the conditional distribution function, in: C. R. Math. Acad. Sci. Paris, 2010, vol. 348, no 17-18, pp. 1015–1019.

    http://dx.doi.org/10.1016/j.crma.2010.08.003
  • 52J. Friedman, T. Hastie, R. Tibshirani.

    Sparse inverse covariance estimation with the graphical lasso, in: Biostatistics, 2008, vol. 9, no 3, pp. 432–441.
  • 53C. Giraud, S. Huet, N. Verzelen.

    Graph selection with GGMselect, in: Statistical applications in genetics and molecular biology, 2012, vol. 11, no 3.
  • 54T. Hansen, U. Zwick.

    Lower Bounds for Howard's Algorithm for Finding Minimum Mean-Cost Cycles, in: ISAAC (1), 2010, pp. 415-426.
  • 55S. Herrmann, P. Vallois.

    From persistent random walk to the telegraph noise, in: Stoch. Dyn., 2010, vol. 10, no 2, pp. 161–196.

    http://dx.doi.org/10.1142/S0219493710002905
  • 56J. Hu, W.-C. Wu, S. Sastry.

    Modeling subtilin production in bacillus subtilis using stochastic hybrid systems, in: Hybrid Systems: Computation and Control, Springer, 2004, pp. 417–431.
  • 57R. Keinj, T. Bastogne, P. Vallois.

    Multinomial model-based formulations of TCP and NTCP for radiotherapy treatment planning, in: Journal of Theoretical Biology, June 2011, vol. 279, no 1, pp. 55-62. [ DOI : 10.1016/j.jtbi.2011.03.025 ]

    http://hal.inria.fr/hal-00588935/en
  • 58R. Koenker.

    Quantile regression, Cambridge university press, 2005, no 38.
  • 59Y. A. Kutoyants.

    Statistical inference for ergodic diffusion processes, Springer Series in Statistics, Springer-Verlag London Ltd., London, 2004, xiv+481 p.
  • 60C. Lacaux.

    Real Harmonizable Multifractional Lévy Motions, in: Ann. Inst. Poincaré., 2004, vol. 40, no 3, pp. 259–277.
  • 61L. Lebart.

    On the Benzecri's method for computing eigenvectors by stochastic approximation (the case of binary data), in: Compstat 1974 (Proc. Sympos. Computational Statist., Univ. Vienna, Vienna, 1974), Vienna, Physica Verlag, 1974, pp. 202–211.
  • 62B. Lesner, B. Scherrer.

    Non-Stationary Approximate Modified Policy Iteration, in: ICML 2015, Lille, France, July 2015.

    https://hal.inria.fr/hal-01186664
  • 63T. Lyons, Z. Qian.

    System control and rough paths, Oxford mathematical monographs, Clarendon Press, 2002.

    http://books.google.com/books?id=H9fRQNIngZYC
  • 64N. Meinshausen, P. Bühlmann.

    High-dimensional graphs and variable selection with the lasso, in: The Annals of Statistics, 2006, pp. 1436–1462.
  • 65J.-M. Monnez.

    Approximation stochastique en analyse factorielle multiple, in: Ann. I.S.U.P., 2006, vol. 50, no 3, pp. 27–45.
  • 66J.-M. Monnez.

    Stochastic approximation of the factors of a generalized canonical correlation analysis, in: Statist. Probab. Lett., 2008, vol. 78, no 14, pp. 2210–2216.

    http://dx.doi.org/10.1016/j.spl.2008.01.088
  • 67J.-M. Monnez.

    Convergence d'un processus d'approximation stochastique en analyse factorielle, in: Publ. Inst. Statist. Univ. Paris, 1994, vol. 38, no 1, pp. 37–55.
  • 68E. Nadaraya.

    On non-parametric estimates of density functions and regression curves, in: Theory of Probability & Its Applications, 1965, vol. 10, no 1, pp. 186–190.
  • 69I. Post, Y. Ye.

    The simplex method is strongly polynomial for deterministic Markov decision processes, arXiv:1208.5083v2, 2012.
  • 70M. Puterman.

    Markov Decision Processes, Wiley, New York, 1994.
  • 71B. Roynette, P. Vallois, M. Yor.

    Brownian penalisations related to excursion lengths, VII, in: Annales de l'IHP Probabilités et statistiques, 2009, vol. 45, no 2, pp. 421–452.
  • 72F. Russo, P. Vallois.

    Stochastic calculus with respect to continuous finite quadratic variation processes, in: Stochastics: An International Journal of Probability and Stochastic Processes, 2000, vol. 70, no 1-2, pp. 1–40.
  • 73F. Russo, P. Vallois.

    Elements of stochastic calculus via regularization, in: Séminaire de Probabilités XL, Berlin, Lecture Notes in Math., Springer, 2007, vol. 1899, pp. 147–185.

    http://dx.doi.org/10.1007/978-3-540-71189-6_7
  • 74B. Scherrer, M. Ghavamzadeh, V. Gabillon, B. Lesner, M. Geist.

    Approximate Modified Policy Iteration and its Application to the Game of Tetris, in: Journal of Machine Learning Research, 2015, vol. 16, pp. 1629–1676, A paraître.

    https://hal.inria.fr/hal-01091341
  • 75B. Scherrer, B. Lesner.

    On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes, in: NIPS 2012 - Neural Information Processing Systems, South Lake Tahoe, United States, December 2012.

    https://hal.inria.fr/hal-00758809
  • 76B. Scherrer.

    Performance Bounds for Lambda Policy Iteration and Application to the Game of Tetris, in: Journal of Machine Learning Research, January 2013, vol. 14, pp. 1175-1221.

    https://hal.inria.fr/hal-00759102
  • 77B. Scherrer.

    Approximate Policy Iteration Schemes: A Comparison, in: ICML - 31st International Conference on Machine Learning - 2014, Pékin, China, June 2014.

    https://hal.inria.fr/hal-00989982
  • 78B. Scherrer.

    Improved and Generalized Upper Bounds on the Complexity of Policy Iteration, in: Mathematics of Operations Research, February 2016, Markov decision processes ; Dynamic Programming ; Analysis of Algorithms. [ DOI : 10.1287/moor.2015.0753 ]

    https://hal.inria.fr/hal-00829532
  • 79P. Vallois, C. S. Tapiero.

    Memory-based persistence in a counting random walk process, in: Phys. A., 2007, vol. 386, no 1, pp. 303–307.

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  • 80P. Vallois.

    The range of a simple random walk on Z, in: Advances in applied probability, 1996, pp. 1014–1033.
  • 81N. Villa-Vialaneix.

    An introduction to network inference and mining, 2015, http://wikistat.fr/, (consulté le 22/07/2015).

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  • 82Y. Ye.

    The Simplex and Policy-Iteration Methods Are Strongly Polynomial for the Markov Decision Problem with a Fixed Discount Rate, in: Math. Oper. Res., 2011, vol. 36, no 4, pp. 593-603.


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