Journal Paper
- D. Barrera, S. Crepey, B. Diallo, G. Fort, E. Gobet and V.
Stazhinksi. Stochastic
Approximation Schemes for Economic Capital and Risk Margin
Computations, Accepted for publication in
ESAIM Proc (CEMRACS 2017), October 2018.
- G. Fort, B. Jourdain, T. Lelièvre and G.
Stoltz. Convergence
and Efficiency of Adaptive Importance Sampling techniques with
partial biasing.
Submitted, November 2016. Revised in July 2017. Revised in
December 2017. Accepted for publication in Journal of
Statistical Physics, February 2018.
- G. Fort, E. Ollier and A. Leclerc-Samson.
Stochastic Proximal Gradient Algorithms for Penalized Mixed
Models. Submitted, April 2017. arXiv:1704.08891; Revised in
October 2017. Accepted for publication in Statistics and
Computing, January 2018.
- G. Fort, E. Gobet and E. Moulines. MCMC
design-based non-parametric regression for rare event.
Application to nested risk computation. Monte
Carlo Methods and Applications, 23(1):21--42, 2017.
- G. Morral, P. Bianchi and G. Fort. Success and Failure of
Adaptation-Diffusion Algorithms for Consensus in Multi-Agent
Networks. IEEE Trans. Signal
Processing, 65(11):2798-2813, 2017.
- Y. Atchadé, G. Fort and E. Moulines. On
perturbed proximal gradient algorithms, Submitted in
February 2014 under the title "On stochastic proximal gradient
algorithms". arXiv:1402:2365 math.ST. Revised in Jan16 and
Nov16. JMLR, 18(10):1-33, 2017.
- H. Braham, S. Ben Jemaa, G. Fort, E. Moulines and B.
Sayrac. Spatial
prediction under location uncertainty in cellular networks,
arXiv:1510:03638, IEEE Trans. Wireless Communications,
15(11):7633-7643, 2016.
- H. Braham, S. Ben Jemaa, G. Fort, E. Moulines and B.
Sayrac. Fixed Rank
Kriging for Cellular Coverage Analysis. arXiv:1505:07062,
IEEE Trans. Vehicular Technology, 66(5):4212-4222, 2016.
- A. Schreck, G. Fort, E. Moulines and M. Vihola.Convergence
of Markovian Stochastic Approximation with discontinuous
dynamics .
arXiv math.ST 1403.6803, submitted in March 2014.
SIAM J. Control Optim.,54(2):866-893, 2016.
- G. Fort, B. Jourdain, T. Lelièvre and G. Stoltz.
Self-Healing Umbrella Sampling: convergence and efficiency.
arXiv math.PR 1410.2109, submitted in October 2014. Revised in
April 2015, Accepted Nov 15. Statistics and Computing,
27(1):147-168, 2017.
- A. Schreck, G. Fort, S. Le Corff and
E. Moulines. A
shrinkage-thresholding Metropolis adjusted Langevin algorithm
for Bayesian variable selection. arXiv math.ST 1312.5658.
IEEE J. of Selected Topics in Signal Processing, 10(2):366-375,
2016.
- A. Durmus, G. Fort and E. Moulines. Subgeometric
rates of convergence rates in Wasserstein distance for Markov
chains.
arXiv:1402.4577
math.PR. Accepted for publication in Ann. Inst.
Henri Poincaré, 52(4):1799-1822, 2016.
- G. Fort. Central
Limit Theorems for Stochastic Approximation with Controlled
Markov Chain Dynamics. EsaimPS, 19:60-80, 2015. arXiv math.PR 1309.3116
- C. Andrieu, G. Fort and M. Vihola. Quantitative
convergence rates for sub-geometric Markov chains. Advances
in Applied Probability, 52(2):391-404, 2015. arXiv
math.PR 1309.0622
- G. Fort, B. Jourdain, E. Kuhn, T. Lelièvre and G. Stoltz. Convergence of the
Wang-Landau algorithm. Math. Comp.,
84:2297-2327, 2015. arXiv:1207.6880 [math.PR]
- G. Fort, B. Jourdain, E. Kuhn, T. Lelièvre and G. Stoltz. Efficiency of the
Wang-Landau algorithm. App. Math. Res. Express,
2914(2):275-311, 2014. arXiv:1310.6550.
- R. Bardenet,
O. Cappé, G. Fort and B. Kegl. Adaptive
MCMC with Online Relabeling. (accepted
for publication in 2013) Bernoulli, 21(3):1304-1340, 2015. arXiv:1210.2601
[stat.CO]
- P. Bianchi, G. Fort and W. Hachem. Performance
of a Distributed Stochastic Approximation Algorithm,
IEEE Trans. on Information Theory, 59(11):7405-7418, 2013.
- S. Le Corff and G. Fort. Online
Expectation Maximization-based algorithms for inference in
Hidden Markov Models. Electronic
Journal of Statistics, 7:763-792, 2013. arXiv math.ST 1108-3968.
Supplement paper,
math.ST 1108-4130.
- G. Fort, E. Moulines, P. Priouret and P. Vandekerkhove. A
Central Limit Theorem for Adaptive and Interacting Markov
Chains.arXiv:1107.2574
Supplement
paper Bernoulli
20(2):457-485, 2014.
- A. Schreck, G. Fort and E. Moulines. Adaptive
Equi-energy sampler : convergence and illustration. ACM
Transactions
on Modeling and Computer Simulation (TOMACS), 23(1):Article 5 -
27 pages, 2013.
- S. Le Corff and G. Fort. Convergence
of a particle-based approximation of the Block online
Expectation Maximization algorithm,
ACM Transactions on Modeling and Computer Simulation (TOMACS)
23(1):Article2 - 22 pages, 2013.
- G. Fort, E. Moulines, P. Priouret and P. Vandekerkhove. A
simple variance inequality for U-statistics of a Markov chain
with Applications.
Statistics & Probability Letters 82(6):1193-1201, 2012.
- G. Fort, E. Moulines and P. Priouret. Convergence
of adaptive and interacting Markov chain Monte Carlo algorithms.
Ann. Statist. 39(6):3262-3289,
2012. [Supplementary
material],
- Y. Atchadé and G. Fort. Limit theorems for
some adaptive MCMC algorithms with subgeometric kernels, part II. Bernoulli
18(3):975-1001, 2012.
- M. Kilbinger, D. Wraith, C. P. Robert, K.
Benabed, O. Cappé, J.F.Cardoso, G. Fort, S. Prunet, and
F.R.Bouchet. Bayesian model comparison in cosmology with
Population Monte Carlo. MNRAS 405(4):2381-2390, 2010.
ArXiv
astro-ph.CO/0912.1614
- P. Etoré, G. Fort, B. Jourdain and E. Moulines.
On
adaptive stratification. Annals
of Operations Research 189(1):127-154, 2011.
ArXiv math.PR/0809.1135
- Y. Atchadé and G. Fort. Limit
theorems
for
some adaptive MCMC algorithms with subgeometric kernels. Bernoulli 16(1):116-154,
2010. ArXiv math.PR/0807.2952
- S. Connor and G. Fort. State-dependent
Foster-Lyapunov criteria for subgeometric convergence of Markov
chains. Stochastic Processes Appl.
119:4176-4193, 2009 ArXiv math.PR/0901.2453
- D. Wraith, M. Kilbinger, K.
Benabed, O. Cappé, J.F.Cardoso, G. Fort, S. Prunet and C. P.
Robert. Estimation
of
cosmological
parameters using adaptive importance sampling. Phys.Rev.
D. 80(2), 2009. ArXiv stat.CO/0903.0837
- R.
Douc, G. Fort, E. Moulines and P. Priouret. Forgetting
of
the
initial distribution for Hidden Markov Models.
Stoch.
Process Appl, 119(4): 1235-1256, 2009.
ArXiv math.ST/0703836
- R. Douc, G. Fort
and A. Guillin. Subgeometric
rates
of
convergence
of f-ergodic strong Markov processes. Stoch. Process Appl,
119(3):897-923, 2009. ArXiv math.ST/0605791
- G.
Fort,
S.
Meyn, E. Moulines and P. Priouret. The
ODE
method
for the stability of skip-free Markov Chains with
applications to MCMC.
Ann. Appl. Probab. 18(2) :664-707,
2008.
- F.
Forbes and G. Fort. A convergence theorem for Variational EM-like
algorithms : application to image segmentation. IEEE
Transactions on Image Processing, 16(3):824-837,2007
MATLAB
Codes
- G.
Fort, S. Lambert-Lacroix, J. Peyre. Réduction
de
dimension
dans
les modèles généralisés : Application à la classification de
données issues des biopuces. Journal de la SFDS,
146(1-2):117-152,2005.
Matlab
codes and Data set. Erratum
on the research report TR0471
- G. Fort and S.
Lambert-Lacroix. Classification
using
Partial
Least
Squares
with Penalized Logistic Regression. Bioinformatics,
21(7):1104-1111, 2005. Matlab
codes and Data set.
- G. Fort and G. O. Roberts. Subgeometric
ergodicity
of
strong Markov processes. Ann. Appl.
Probab. 15(2):1565-1589, 2005.
- R. Douc, G. Fort, E. Moulines
and P. Soulier. Practical
drift
conditions
for
subgeometric rates of convergence. Ann.
Appl. Probab. 14(3) :1353-1377, 2004.
- G. Fort, E. Moulines, G.O.
Roberts and J.S. Rosenthal. On
the
geometric
ergodicity of hybrid samplers. J. Appl. Probab.
40(1):123-146, 2003.
- G. Fort and E. Moulines. Polynomial
ergodicity
of
Markov transition kernels. Stochastic Processes Appl.
103(1):57-99, 2003.
- G. Fort and E. Moulines. Convergence
of
the
Monte-Carlo EM for curved exponential families. Ann.
Stat. 31(4):1220-1259, 2003.
- G. Fort and E. Moulines.
V-subgeometric ergodicity for a Hastings-Metropolis algorithm. Stat.
Probab. Lett. 49(4):401-410,2000.
Chapters in books
- Y. Atchadé, G. Fort, E. Moulines and P. Priouret. In D.
Barber, A. T. Cemgil and S. Chiappia, editors. Bayesian Time Series Models,
Cambridge Univ. Press, 2011. Chapter 2 : Adaptive Markov chain Monte Carlo : Theory and Methods,
33-53.
- G. Fort, E. Moulines and P. Soulier. In O. Cappé, E.
Moulines and T. Ryden, editors. Inference
in Hidden Markov Models, Springer 2005. Chapter 14: Elements
of Markov Chain Theory, 511-562.
Conference Proceedings
- G. Fort, L. Risser, Y. Atchadé and E. Moulines. Stochastic
FISTA algorithms: so fast ? SSP, accepted in April 2018
- G. Fort, L. Risser, E. Moulines, E. Ollier and A.
Leclerc-Samson. Algorithmes Gradient-Proximaux stochastiques. GRETSI, September 2017.
- G. Morral, P. Bianchi and G. Fort. Success
and Failure of Adaptation-Diffusion Algorithms for Consensus in
Multiagent Networks.
Accepted for publication in the proceedings of the 53rd IEEE
Conference on Decision and Control (CDC 2014), December
2014.
- H. Braham, S. Ben Jemaa, G. Fort, E. Moulines and B.
Sayrac. Coverage Mapping Using Spatial
Interpolation With Field Measurements. Accepted
for presentation and publication in the proceedings to : IEEE
PIMRC - Mobile and Wireless Networks 2014, September 2014.
- H. Braham, S. Ben Jemaa, G. Fort, E.
Moulines and B. Sayrac. Low
complexity Spatial Interpolation For Cellular Coverage Analysis.
Accepted for presentation and publications in the proceedings to
: WiOpt 2014, May 2014.
- G. Morral, P. Bianchi, G. Fort and J. Jakubowicz.
Approximation stochastique distribuée : le coût de la non
bistochasticité. GRETSI,
September 2013.
- G. Morral, P. Bianchi, G. Fort and J. Jakubowicz. Distributed
Stochastic
Approximation: The Price of Non-double Stochasticity. ASILOMAR November 2012.
- R. Bardenet, O. Cappé, G. Fort and B. Kegl. Adaptive
Metropolis
with online relabeling. (Supplementary
paper). JMLR Workshop and
Conference Proceedings Vol 22, p.91-99, AISTATS 2012
- S. Le Corff, G. Fort and E. Moulines. New
Online-EM
algorithms
for general Hidden Markov models. Application to the SLAM, Proceedings of the 10th
International Conference on Latent Variable Analysis and Signal
Separation (LVA-ICA), Springer-Verlag Berlin, Heidelberg
pages 131-138, 2012.
- P. Bianchi, G. Fort, W. Hachem and J. Jakubowicz. Performance
Analysis
of
a Distributed On-Line Estimator for Sensor Networks. Proceedings
of the 19th European Signal Processing Conference (EUSIPCO),
pages 1030-1034, 2011.
- S. Le Corff, G. Fort and E. Moulines. Un
algorithme
EM
récursif
pour le SLAM. Proceedings du Groupe d'Etudes du Traitement du Signal
et des Images (GRETSI), 2011.
- P. Bianchi, G. Fort, W. Hachem and J. Jakubowicz. Sur
un
algorithme
de Robbins-Monro distribué. Proceedings
du Groupe d'Etudes du Traitement du Signal et des Images
(GRETSI), 2011.
- S. Le Corff, G. Fort and E. Moulines.
Online
Expectation-Maximization
algorithm
to solve the SLAM problem, Proceedings of the 2011 IEEE
Statistical Signal Processing Workshop (SSP), pages 225-228, 2011.
- S. Le Corff and G. Fort. Block Online EM for Hidden Markov
Models with general state space, 2011. Proceedings of
International Conference Applied Stochastic Models and Data
Analysis (ASMDA), 2011.
- P. Bianchi, G. Fort, W. Hachem and J. Jakubowicz. Convergence
of
a
distributed parameter estimator for sensor network with local
averaging of the estimates. Proceedings of the International
Conference on Acoustics, Speech and Signal Processing (ICASSP),
pages 3764-3767, 2011.
- G. Fort, S. Meyn, E. Moulines and P. Priouret. ODE methods
for Markov chain stability with applications to MCMC. Proceedings
of the 1st International Conference on Performance Evaluation
Methodologies and Tools, Valuetools, Art. 42, 2006.
- G. Fort and S. Lambert-Lacroix. Ridge-Partial
Least
Squares
for Generalized Linear Models with binary response. COMPSTAT'04,
Proceedings in Computational
Statistics, pages 1019-1026, 2004.
- G. Fort and E. Moulines, and P. Soulier. On the convergence
of iterated random maps with applications to the MCEM algorithm. Computational
Statistics, August, 1998.
- G. Fort, O. Cappé, E. Moulines,
and P. Soulier. Optimization via simulation for maximum likelihood
estimation in incomplete data models. In Proc. IEEE Workshop
on Stat. Signal and Array Proc., pages 80-83, 1998.
Technical Report
- S.Crepey, G. Fort, E. Gobet and U.
Stazhynski. Quantification d'incertitude pour l'Approximation
Stochastique (soumis pour présentation dans une conférence
francophone - "teaser" du papier long ci-après).
- S. Crepey, G. Fort, E. Gobet and U. Stazhynski. Uncertainty
quantification for Stochastic Approximation limits using Chaos
Expansion. November 2017. HAL-01629952. Revised in March
2018. Revised in January 2019.
- G. Fort. Fluid
limit-based
tuning
of some hybrid MCMC samplers. Dec 2007.
- C. Andrieu and G. Fort. Explicit
control
of
subgeometric ergodicity. Rapport de Recherche, 05:17, 2005.
- G. Fort. Partial
Least
Squares
for classification and feature selection in Microarray gene
expression data. Dec. 2004.
- G. Fort. Computable
bounds
for
V-geometric
ergodicity of Markov transition kernels. Rapport de
Recherche, Univ. J. Fourier, RR 1047-M.
PhD
Thesis and HDR
- G. Fort. Habilitation à Diriger les Recherches "Méthodes
de
Monte
Carlo et Chaînes de Markov pour la simulation".
Univ. Paris Dauphine, Feb. 2010. (website)
- G. Fort. PhD thesis. "Contrôle explicite d'ergodicité de
chaînes de Markov : application à l'analyse de convergence de
l'algorithme Monte Carlo EM". Univ. Paris VI, June
2001. Inist Number : T139824