ABCDEFGHIJKLMNOPQRSTUVWXYZAAABACADAEAFAGAHAIAJAK
1
Binôme 1Horaire de passage VISIO binôme 1Jury 1 ou 2Contraintes binôme 1Binôme 2Horaire de passage VISIO binôme 2Jury 1 ou 2Contraintes binôme 2
2
3
Convergence of Markovian Stochastic Approximation with
discontinuous dynamics . A. Schreck, G. Fort, E. Moulines
and M. Vihola. (2016).
4
On perturbated proximal gradient algorithm.
Y. Atchadé, G. Fort and E. Moulines (2016)
5
Tsitsiklis, John N. “Asynchronous Stochastic Approximation
and Q-Learning.” Mach/ine Learning16, no. 3
(September 1, 1994): 185–202.
https://doi.org/10.1007/BF00993306.
6
Riemannian manifold Langevin and
Hamiltonian Monte Carlo methods.
M. Girolami,d. (2011). 
Partie Riemanian Hamiltonian Monte Carlo !		Maher BILLON & Irène MISGUICH
7
Riemannian manifold Langevin and
Hamiltonian Monte Carlo methods.
M. Girolami,Calderhead. (2011).
Partie Langevin		DAMMAK iyed
8
Optimization by gradient boosting, G. BIAU and B. CADRE, 
https://hal.archives-ouvertes.fr/hal-01562618		MASSARO et LASSALLEArthur Bendif
9
G. Fort, L. Risser, Y. Atchadé and E. Moulines. Stochastic FISTA algorithms: so fast ? . Workshop in Statistical Signal Processing (SSP), June 2018.
10
Reliable ABC model choice via random forests,
Pudlo, Marin, Cornuet, Estoup, Gautier and Robert (2016)		Mohamed KASSOUL
11
ADAPTIVE PARALLEL TEMPERING ALGORITHM. Blazej Miasojedow, Eric Moulines, Matti Viholaa
12
Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: Application to HIV dynamics model. A Samson, M Lavielle, F Mentré
Computational Statistics & Data Analysis 51 (3), 1562-1574
13
An adaptive version for the Metropolis adjusted Langevin
algorithm with a truncated drift -
http://dept.stat.lsa.umich.edu/~yvesa/atmala.pdf
14
On the convergence of Stochastic Approximations under a subgeometric ergodic Markov dynamic V Debavelaere, S Durrleman, S Allassonnière.
15
Robust Adaptive Importance Sampling for Normal Random Vectors. Benjamin Jourdain, Jérôme LelongZhengyi Wang Nathalie Heinzelmeier
16
Estimation of demo-genetic model probabilities with
Approximate Bayesian Computation using linear discriminant
analysis on summary statistics,
Estoup, Lombaert, Marin, Guillemaud, Pudlo, Robert
and Cornuet (2012) 
https://link.springer.com/content/pdf/10.1007%2Fs00122-017-2922-4.pdf
17
Online EM Algorithm for Hidden Markov Models Olivier CappéGabriel Libardi et Federico Méndez
18
Mixtures of stochastic differential equations with
random effects: Application to data clustering.
19
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
20
Variance reduction for Markov chains with application to MCMC
D Belomestny, L Iosipoi, E Moulines, A Naumov, S Samsonov
Statistics and Computing, 1-25
21
Fast Incremental Expectation Maximization for non-convex finite-sum optimization: non asymptotic convergence bounds. Fast Incremental Expectation Maximization for non-convex finite-sum optimization: non asymptotic convergence bounds
G Fort, P Gach, E Moulines
22
Geometric ergodicity of the bouncy particle sampler
A Durmus, A Guillin, P Monmarché
Annals of Applied Probability 30 (5), 2069-2098
23
Bringing ABC inference to the machine learning realm: AbcRanger, an optimized random forests library for ABC
FD Collin, A Estoup, JM Marin, L Raynal
JOBIM 2020 2020
24
Leveraging the Exact Likelihood of Deep Latent Variable Models. Pierre-Alexandre Mattei, Jes Frellsen. Ayoub Elmoussaoui
25
Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder

C. Chadebec, E. Thibeau-Sutre, N. Burgos, and S. Allassonnière. IEEE Pattern Analysis and Machine Intelligence

Oscar Tron +Marc-Antoine WilkLubin Longuépée, Oualid Chabane
26
Georgios Arvanitidis, Lars Kai Hansen, and Søren Hauberg. Latent space oddity: on the
curvature of deep generative models. arXiv preprint arXiv:1710.11379, 2017		Marie Tcheng
&
Yanis Gomes
27
Deterministic Approximate EM algorithm; Application to the Riemann approximation EM and the tempered EM. Thomas Lartigue, Stanley Durrleman, Stéphanie Allassonnière. Submitted. (2020)
28
hyperspherical variational autoencodeurMikhail Kataevskii & Kenji Chikhaoui
29
Irreversible local Markov chains with rapid convergence
towards equilibrium Sebastian C. Kapfer and Werner Krauth
30
Uncovering the structure of clinical EEG signals with self-supervised learning (Banville et al., 2021)Corentin Marquet & Lucile MagnierBeatrice Marconi, Samuel Molano Quintana
31
NEO: Non Equilibrium Sampling on the Orbit of a Deterministic Transform (https://papers.nips.cc/paper/2021/file/8dd291cbea8f231982db0fb1716dfc55-Paper.pdf)
32
On Estimation in Latent Variable Models (https://proceedings.mlr.press/v139/fang21a.html)
33
Variational inference via Wasserstein
gradient flows : https://arxiv.org/pdf/2205.15902.pdf		Bono Alexandre
De Saint Savin Thomas		Tranié Alexandre
Almairac Louis
34
importance weighted autoencoders https://arxiv.org/pdf/1509.00519 Meryem Liab / Diae Archidi
35
On the properties of variational
approximations of Gibbs posteriors https://arxiv.org/pdf/1506.04091
36
Differentiable Expectation-Maximisation and Applications to Gaussian Mixture Model Optimal Transport
37
Hamiltonian Variational Auto-Encoder : https://arxiv.org/pdf/1805.11328
38
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching (ICML 2025)
https://arxiv.org/abs/2504.11713		Nathan Roos + Réda OuzzaneOrel Mazor + Honoré Boïarsky
39
On Sampling with Approximate Transport Maps (L.Grenioux, A.Durmus, E.Moulines, M.Gabrié, 2023) https://arxiv.org/pdf/2302.04763Fatimazahrae Akhyar
40
XGBoost: A Scalable Tree Boosting System (Tianqi Chen, Carlos Guestrin)Laure Guerin et Selma SadoqiJavier Molinero Araguas + Enia Ardid Prats
41
TabPFN: Accurate predictions on small data with a tabular foundation modelAbdel Zighem et Paul Enee
Vladislav Antipin + Sebastian Straut
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100