| Patricia
Reynaud-Bouret
Laboratoire
J.A. Dieudonné
Université Côte d'Azur CNRS UMR 7351 Parc Valrose, 06108 Nice, Cedex 2, France +33 (0) 4 89 15 04 96 Patricia.Reynaud-Bouret@univ-cotedazur.fr |
 |
Job Offer
- Post Doctoral Position available on "Testing mean-field behavior" (ANR ChaMaNe)
| 2020
: |
Pierre Faurre
prize of the French Academy
of Science |
| 2019- : | Director of the NeuroMod
Institute (in charge of the federation of the
corresponding scientific community since 2014) (Vice-director :Alexandre Muzy) |
| 2016- : | In
charge of the construction, then administration of
the Master of Science Mod4NeuCog,
open since September 2018, currently pedagogical coordinator (Director Ingrid Bethus) |
| 2008- : | CNRS researcher at Laboratoire JA
Dieudonné (Directeur de Recherche since
2014) |
| 2003-2008 : | |
| 2002-2003 : | Postdoctoral
Studies at the School of Mathematics (Georgia Institute of
Technology, |
| 1999-2002 : |
PhD Thesis
at Département de
Mathématiques d'Orsay (Paris XI)
PhD advisor : Pascal
Massart
|
- Neurosciences and cognition : analysis of spikes trains and functional connectivity, learning models.
- Adaptive Statistics : Model selection, Lasso and multiple testing
- Point processes : Hawkes, Poisson, Aalen
- Concentration inequalities
- PhD Students :
| Salima El Kolei (2009-2012) | also supervised by Frédéric Patras | ex-statistician expert for SAFRAN, currently teacher at ENSAI (Rennes) |
| Laure Sansonnet (2009-2013) | also supervised by Vincent Rivoirard | ex-PostDoc UCL, Belgium, currently MdC at AgroParisTech |
| Mélisande
Albert (2012-2015) |
also supervised by Magalie Fromont | ex-PostDoc Grenoble, LJK/GIPSA, currently MdC at Insa-Toulouse |
| Julien Chevallier (2013-2016) | also supervised by François Delarue | ex-PostDoc Cergy-Pontoise, currently MdC at Grenoble. |
| Giulia Mezzadri
(2016- ) |
also supervised by Fabien Mathy and Thomas Laloë | |
| Cyrille Mascart (2016- ) |
also supervised by Alexandre Muzy | |
| Laurent Dragoni (2017- ) | also supervised by Karim Lounici and Rémi Flamary | |
| Cuong Tien Phi (2018- ) |
also supervised by Eva Löcherbach |
- Other Master Students
| Amel Rouis (2011) | biostatistician at CHU - Nice |
| José Bonnet (2013) | research engineer EDF-LJAD on the prediction of sismic activity |
| Imen Chaarana (2015) | computer scientist for Talent Consulting |
| Valentina Mazzi (2016) | Erasmus exchange from Verona University |
| Hendjy Taraud (2017) | in partnership with deligeo |
| David Maillot (2018) | in
partnership with deligeo
|
| Sepideh Iranfar (2019) | |
| Jacopo Baldi (2020) |
Publications
[30] Phi, Tien Cuong; Muzy, Alexandre; Reynaud-Bouret, Patricia Event scheduling algorithms with Kalikow decomposition for simulating potentially infinite neuronal networks, SN Computer Science, 1(35) (2020).
[29] Ost, Guilherme; Reynaud-Bouret, Patricia Sparse space-time models: concentration inequalities and Lasso, Annales de l'IHP, Probabilités et Statistiques, to appear (2019).
[28] Coeurjolly, Jean-François; Reynaud-Bouret, Patricia A concentration inequality for inhomogeneous Neymann-Scott processes, Statistics and Probability Letters, 148, 30-34 (2019).
[27] Hodara, Pierre; Reynaud-Bouret, Patricia Exponential inequality for chaos based on sampling without replacement, Statistics and Probability Letters, 146, 65-69 (2019).
[26] Hunt, Xin Jiang; Reynaud-Bouret, Patricia; Rivoirard, Vincent; Sansonnet, Laure; Willett, Rebecca A data-dependent weighted LASSO under Poisson noise, IEEE Transactions on Information Theory, 65(3), 1589-1613 (2019).
[25] Picard, Franck; Reynaud-Bouret, Patricia; Roquain, Etienne Continuous testing for Poisson process intensities: a new perspective on scanning statistics, Biometrika, 105(4), 931-944 (2018).
[24] Lambert, Régis; Tuleau-Malot, Christine; Bessaih, Thomas; Rivoirard, Vincent; Bouret, Yann; Leresche, Nathalie; Reynaud-Bouret, Patricia Reconstructing the functional connectivity of multiple spike trains using Hawkes models, Journal of Neuroscience Methods, 297, 9-21 (2018) .
[23] Albert, Mélisande ; Bouret, Yann ; Fromont, Magalie ; Reynaud-Bouret , Patricia Surrogate data methods based on a shuffling of the trials for synchrony detection: the centering issue, Neural Computation, 28(11), 2352-2392 (2016).
[22] Fromont, Magalie ; Lerasle, Matthieu ; Reynaud-Bouret, Patricia Family Wise Separation Rates for multiple testing, Annals of Statistics, 44(6), 2533-2563 (2016).
[21] Lerasle, Matthieu ; Magalhães, Nelo ; Reynaud-Bouret, Patricia Optimal kernel selection for density estimation High Dimensional Probability VII : The Cargese Volume, Prog. Probab. 71, Birkhaüser, 425-460 (2016).
[20] Chevallier, Julien ; Cáceres, María José ; Doumic, Marie ; Reynaud-Bouret, Patricia Microscopic approach of a time elapsed neural model, Mathematical Models and Methods in Applied Sciences, 25(14), 2669-2719 (2015).
[19] Albert, Mélisande ; Bouret, Yann ; Fromont, Magalie ; Reynaud-Bouret, Patricia Bootstrap and permutation tests of independence for point processes, Annals of Statistics, 43(6), 2537-2564 (2015).
[18] Tuleau-Malot, Christine ; Rouis, Amel ; Grammont, Franck ; Reynaud-Bouret, Patricia Multiple tests based on a Gaussian approximation of the Unitary Events method with delayed coincidence count, Neural Computation, 26(7), 1408-1454 (2014).
[17] Reynaud-Bouret, Patricia ; Rivoirard, Vincent ; Grammont , Franck ; Tuleau-Malot, Christine Goodness-of-fit tests and nonparametric adaptive estimation for spike train analysis, Journal of Mathematical Neuroscience, 4:3 (2014).
[16] Reynaud-Bouret, Patricia Concentration inequalities, counting processes and adaptive statistics, ESAIM Proc. (proceedings of "Journées MAS 2012"), 44, 79-98 (2014).
[15] Reynaud-Bouret, Patricia ; Rivoirard, Vincent; Tuleau-Malot, Christine Inference of functional connectivity in Neurosciences via Hawkes processes, 1st IEEE Global Conference on Signal and Information Processing, Austin, Texas (2013).
[14] Hansen, Niels R. ; Reynaud-Bouret, Patricia ; Rivoirard, Vincent Lasso and probabilistic inequalities for multivariate point processes, Bernoulli, 21(1), 83-143 (2015).
[13] Fromont, Magalie ; Laurent, Béatrice ; Reynaud-Bouret, Patricia The two-sample problem for Poisson processes: adaptive tests with a non-asymptotic wild bootstrap approach, Annals of Statistics, 41(3), 1431-1461 (2013).
[12] Fromont, Magalie ; Laurent, Béatrice ; Lerasle, Matthieu ; Reynaud-Bouret, Patricia Kernels based tests with non-asymptotic bootstrap approaches for two-sample problems, JMLR : Workshop and Conference Proceedings, 23, 25th Annual Conference on Learning Theory, 23.1–23.22 (2012).
[11] Doumic -Jauffret, Marie ; Hoffmann, Marc ; Reynaud-Bouret, Patricia ; Rivoirard, Vincent Nonparametric estimation of the division rate of a size-structured population, SIAM Journal on Numerical Analysis, 50, 925-950 (2012).
[10] Reynaud-Bouret, Patricia ; Rivoirard, Vincent ; Tuleau-Malot, Christine Adaptive density estimation: a curse of support? J. Statist. Plann. Inference, 141, 115-139 (2011).
[9] Fromont, Magalie ; Laurent, Béatrice ; Reynaud-Bouret, Patricia Adaptive test of homogeneity for a Poisson process Ann. Inst. H. Poincaré Probab. Statist. 47 (1), 176-213 (2011).
[8] Reynaud-Bouret, Patricia ; Schbath, Sophie Adaptive estimation for Hawkes processes; application to genome analysis Annals of Statististics, 38(5), 2781-2822 (2010).
[7] Reynaud-Bouret, Patricia ; Rivoirard, Vincent Near optimal thresholding estimation of a Poisson intensity on the real line Electronic Journal of Statistics, 4, 172-238 (2010).
[6] Houdré, Christian ; Marchal, Philippe ; Reynaud-Bouret, Patricia Concentration for norms of infinitely divisible vectors with independent components. Bernoulli, 14(4), 926-948 (2008).
[5] Reynaud-Bouret, Patricia ; Roy, Emmanuel Some non asymptotic tail estimates for Hawkes processes. Bulletin of the Belgian Mathematical Society-Simon Stevin, 13(5), 883-896 (2007) (old version here).
[4] Reynaud-Bouret, Patricia Penalized projection estimators of the Aalen multiplicative intensity. Bernoulli, 12(4), 633-661 (2006).
[3] Reynaud-Bouret, Patricia Compensator and exponential inequalities for some suprema of counting processes. Statistics and Probability Letters, 76(14), 1514-1521 (2006).
[2] Houdré, Christian ; Reynaud-Bouret, Patricia Exponential inequalities, with constants, for U-statistics of order two. Stochastic inequalities and applications, Progr. Probab., 56 Birkhäuser,
[1] Reynaud-Bouret, Patricia Adaptive estimation of the intensity of inhomogeneous Poisson processes via concentration inequalities. Probab. Theory Related Fields 126 (1), 103-153 (2003)
[g] Mascart, Cyrille; Muzy, Alexandre; Reynaud-Bouret, Patricia Discrete event simulation of point processes: a computational complexity analysis on sparse graphs, submitted
[f] Albert, Mélisande ; Bouret, Yann ; Fromont, Magalie ; Reynaud-Bouret, Patricia (preliminary work for [23]) A Distribution Free Unitary Events Method based on Delayed Coincidence Count
[e] Reynaud-Bouret, P. ; Tuleau-Malot, C. ; Rivoirard, V. ; Grammont, F. (preliminary work for [17]) Spike trains as (in)homogeneous Poisson processes or Hawkes processes: non-parametric adaptive estimation and goodness-of-fit tests.
[d] Tuleau-Malot, C. ; Rouis, A. ; Reynaud-Bouret, P. ; Grammont, F. (preliminary work for [18]) Multiple Tests Based on a Gaussian Approximation of the Unitary Events.
[c] Reynaud-Bouret, Patricia ; Rivoirard, Vincent (preliminary work for [7]) Calibration of thresholding rules for Poisson intensity estimation
[b] Reynaud-Bouret, Patricia ; Rivoirard, Vincent (preliminary work for [7]) Adaptive thresholding estimation of a Poisson intensity with infinite support
[a] Houdré, Christian ; Reynaud-Bouret, Patricia (preliminary work for [6]) Concentration for Infinitely Divisible Vectors with Independent Components
HDR Manuscript : Adaptive statistical inference for some point processes (Poisson, Aalen, Hawkes)
Material
- Course on point processes: 1, 2, 3, 4.
- Hawkes and Genomics
- Size-structured population
- Point processes, concentration and adaptive statistics
- Course on Hawkes processes and applications to neuroscience and genomics
- Course on variable detection and multiple testing
2017/2018: Course1 Course2 Basic Tests :
small review (with typos)
GDT Reinforcement learning LJAD-I3S
[1] Lucile Sassatelli (I3S) 21 Octobre 2020
An introduction on Reinforcement Learning and Deep RL
This presentation is meant to provide basics of Reinforcement Learning (RL) and introduce Deep Reinforcement Learning (DRL), for both synthetic and more realistic problems. While applications of RL are typically limited to discrete, low-dimensional constraints, recent advances in Deep RL (DQN for Atari 2600, AlphaGo, and more lately AlphaGo Zero) have demonstrated human-level or super-human performance in complex, high-dimensional spaces. However, DRL remains an active research domain and, as often experienced, is not yet a plug-and-play optimization tool. In this presentation, we will also peek into this challenging aspect of DRL for general control problems such as a robot navigation task, discussing difficulties inherent to RL and most recent approaches to such problems.
(slides)
[2] Luc Lehéricy (LJAD) 18 Novembre 2020
An introduction to the mathematical proofs of reinforcement learning
Numerous reinforcement learning algorithms have been introduced these last few years to efficiently solve an increasing vast array of problems. Despite their variety, most of them rely on a few well-trodden ideas, the main difficulty being to adapt these ideas to a specific situation. The goal of this presentation is to give the mathematical tools to understand two fundamental and easily generalizable toy-models of reinforcement learning: the stochastic bandits and adversarial bandits.
(slides)
[3] Oussama Sabri (I3S) 9 Décembre 2020 (10h)
Neural basis of learning
Human beings are born with the fascination gift of learning. With aid of such, they absorb and assimilate knowledge throughout thier entire life. Reinforcement Learning (RL) is one of the computational methods that attempt to simulate such characteristic in a computational environment capable of learning. However, RL carries different meanings for different communities from mathematics, computer science, neuroscience to psychology, etc. In this talk, I will try to clarify in general the similarities of RL in different domains and in which ways they differ with a focus on the neuro-cognitive part of the brain to understand the mechanisms behind human/animal learning and decision-making