Text-to-speech synthesis in the French electronic mail environment. - Christel Sorin; Raymond Descout; Christian Benoît; Françoise Emerard; C. Fluhr; Danielle Larreur; J. L. Le Saint-Milon; Eric Moulines; R. Peron (ECST, 1987)

Text-to-speech algorithms based on FFT synthesis. - Francis Charpentier; Eric Moulines (ICASSP, 1988)

A diphone synthesis system based on time-domain prosodic modifications of speech. - Christian Hamon; Eric Moulines; Francis Charpentier (ICASSP, 1989)

Detection of the glottal closure by jumps in the statistical properties of the signal. - Renaud J. Di Francesco; Eric Moulines (EUROSPEECH, 1989)

A real-time French text-to-speech system generating high-quality synthetic speech. - Eric Moulines; Françoise Emerard; Danielle Larreur; J. L. Le Saint-Milon; L. Le Faucheur; F. Marty; Francis Charpentier; Christel Sorin (ICASSP, 1990)

Pitch-synchronous waveform processing techniques for text-to-speech synthesis using diphones. - Eric Moulines; Francis Charpentier (Speech Commun., 1990)

Detection of the glottal closure by jumps in the statistical properties of the speech signal. - Eric Moulines; Renaud J. Di Francesco (Speech Commun., 1990)

Voice tranformation using PSOLA technique. - Hélène Valbret; Eric Moulines; Jean-Pierre Tubach (EUROSPEECH, 1991)

Low-delay frequency domain LMS algorithm. - Omar Ait Amrane; Eric Moulines; Maurice Charbit; Yves Grenier (ICASSP, 1992)

Direction finding algorithms using fourth order statistics: asymptotic performance analysis. - Eric Moulines; Jean-François Cardoso (ICASSP, 1992)

Voice transformation using PSOLA technique. - Hélène Valbret; Eric Moulines; Jean-Pierre Tubach (Speech Commun., 1992)

Minimum constrast estimation with applications to array processing. - Jean-François Cardoso; Eric Moulines (ICASSP (4), 1993)

HNS: Speech modification based on a harmonic+noise model. - Jean Laroche; Yannis Stylianou; Eric Moulines (ICASSP (2), 1993)

Asymptotic performance of second order blind separation. - Karim Abed-Meraim; Adel Belouchrani; Jean-François Cardoso; Eric Moulines (ICASSP (4), 1994)

A robustness property of DOA estimators based on covariance. - Jean-François Cardoso; Eric Moulines (IEEE Trans. Signal Process., 1994)

Prediction error methods for time-domain blind identification of multichannel FIR filters. - Karim Abed-Meraim; Pierre Duhamel; David Gesbert; Philippe Loubaton; Sylvie Mayrargue; Eric Moulines; Dirk T. M. Slock (ICASSP, 1995)

Statistical methods for voice quality transformation. - Yannis Stylianou; Olivier Cappé; Eric Moulines (EUROSPEECH, 1995)

High-quality speech modification based on a harmonic + noise model. - Yannis Stylianou; Jean Laroche; Eric Moulines (EUROSPEECH, 1995)

Non-parametric techniques for pitch-scale and time-scale modification of speech. - Eric Moulines; Jean Laroche (Speech Commun., 1995)

Editorial. - Eric Moulines; Yoshinori Sagisaka (Speech Commun., 1995)

Asymptotic performance analysis of direction-finding algorithms based on fourth-order cumulants. - Jean-François Cardoso; Eric Moulines (IEEE Trans. Signal Process., 1995)

The generalized multidelay adaptive filter: structure and convergence analysis. - Eric Moulines; Omar Ait Amrane; Yves Grenier (IEEE Trans. Signal Process., 1995)

Subspace methods for the blind identification of multichannel FIR filters. - Eric Moulines; Pierre Duhamel; Jean-François Cardoso; Sylvie Mayrargue (IEEE Trans. Signal Process., 1995)

Second order blind equalization in multiple input multiple output FIR systems: a weighted least squares approach. - Alexei Gorokhov; Philippe Loubaton; Eric Moulines (ICASSP, 1996)

Subspace methods for blind identification of SIMO-FIR systems. - Eric Moulines; Jean-François Cardoso; Alexei Gorokhov; Philippe Loubaton (ICASSP, 1996)

Regularization techniques for discrete cepstrum estimation. - Olivier Cappé; Eric Moulines (IEEE Signal Process. Lett., 1996)

Time-domain procedures for testing that a stationary time-series is Gaussian. - Eric Moulines; Karim Choukri (IEEE Trans. Signal Process., 1996)

Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models. - Eric Moulines; Jean-François Cardoso; Elisabeth Gassiat (ICASSP, 1997)

Asymptotically invariant Gaussianity test for causal invertible time series. - Roxana Ojeda; Jean-François Cardoso; Eric Moulines (ICASSP, 1997)

A simulated annealing version of the EM algorithm for non-Gaussian deconvolution. - Marc Lavielle; Eric Moulines (Stat. Comput., 1997)

Subspace method for blind identification of multichannel FIR systems in noise field with unknown spatial covariance. - Karim Abed-Meraim; Yingbo Hua; Philippe Loubaton; Éric Moulines (IEEE Signal Process. Lett., 1997)

A subspace algorithm for certain blind identification problems. - Karim Abed-Meraim; Philippe Loubaton; Eric Moulines (IEEE Trans. Inf. Theory, 1997)

On subspace methods for blind identification of single-input multiple-output FIR systems. - Karim Abed-Meraim; Jean-François Cardoso; Alexei Y. Gorokhov; Philippe Loubaton; Eric Moulines (IEEE Trans. Signal Process., 1997)

Prediction error method for second-order blind identification. - Karim Abed-Meraim; Eric Moulines; Philippe Loubaton (IEEE Trans. Signal Process., 1997)

A blind source separation technique using second-order statistics. - Adel Belouchrani; Karim Abed-Meraim; Jean-François Cardoso; Eric Moulines (IEEE Trans. Signal Process., 1997)

A locally stationary semi-Markovian representation for ethernet LAN traffic data. - Sandrine Vaton; Eric Moulines (Broadband Communications, 1998)

Bayesian Analysis of Overdispersed Count Data with Application to Teletraffic Monitoring. - Olivier Cappé; Randal Douc; Eric Moulines; Christian P. Robert (COMPSTAT, 1998)

On the Convergence of Iterated Random Maps with Applications to the MCEM Algorithm. - Gersende Fort; Eric Moulines; Philippe Soulier (COMPSTAT, 1998)

Quasi-Newton method for maximum likelihood estimation of hidden Markov models. - Olivier Cappé; Vincent Buchoux; Eric Moulines (ICASSP, 1998)

Polynomial quasi-harmonic models for speech analysis and synthesis. - Gilles Faÿ; Eric Moulines; Olivier Cappé; Frédéric Bimbot (ICASSP, 1998)

Blind and semi-blind equalization: methods and algorithms. - Vincent Buchoux; Lisa Perros-Meilhac; Olivier Cappé; Eric Moulines (Ann. des Télécommunications, 1998)

An algorithm for maximum likelihood estimation of hidden Markov models with unknown state-tying. - Olivier Cappé; Chafic Mokbel; Denis Jouvet; Eric Moulines (IEEE Trans. Speech Audio Process., 1998)

Continuous probabilistic transform for voice conversion. - Yannis Stylianou; Olivier Cappé; Eric Moulines (IEEE Trans. Speech Audio Process., 1998)

Application of blind second order statistics MIMO identification methods to the blind CDMA forward link channel estimation. - Philippe Loubaton; Eric Moulines (ICASSP, 1999)

Blind knowledge based algorithms based on second order statistics. - Lisa Perros-Meilhac; Pierre Duhamel; Pascal Chevalier 001; Eric Moulines (ICASSP, 1999)

A parametric blind subspace identification: robustness issue. - Lisa Perros-Meilhac; Eric Moulines; Pascal Chevalier 001; Pierre Duhamel (IEEE Commun. Lett., 1999)

Simulation-based methods for blind maximum-likelihood filter identification. - Olivier Cappé; Arnaud Doucet; Marc Lavielle; Eric Moulines (Signal Process., 1999)

Performance of a subspace based semi-blind technique in the UMTS TDD mode context. - Samson Lasaulce; Philippe Loubaton; Eric Moulines (ICASSP, 2000)

On a Perturbation Approach for the Analysis of Stochastic Tracking Algorithms. - Rafik Aguech; Eric Moulines; Pierre Priouret (SIAM J. Control. Optim., 2000)

On the performance of semi-blind subspace-based channel estimation. - Vincent Buchoux; Olivier Cappé; Eric Moulines; Alexei Gorokhov (IEEE Trans. Signal Process., 2000)

In-variance of subspace based estimators. - Jean-François Cardoso; Eric Moulines (IEEE Trans. Signal Process., 2000)

On blind multiuser forward link channel estimation by the subspace method: identifiability results. - Philippe Loubaton; Eric Moulines (IEEE Trans. Signal Process., 2000)

An adaptive broadband estimator of the fractional differencing coefficient. - Clifford M. Hurvich; Eric Moulines; Philippe Soulier (ICASSP, 2001)

Training-based channel estimation and de-noising for the UMTS TDD mode. - Samson Lasaulce; Philippe Loubaton; Eric Moulines; Soodesh Buljore (VTC Fall, 2001)

Estimation of the spectral envelope of voiced sounds using a penalized likelihood approach. - Marine Campedel-Oudot; Olivier Cappé; Eric Moulines (IEEE Trans. Speech Audio Process., 2001)

Blind identification of multipath channels: a parametric subspace approach. - Lisa Perros-Meilhac; Eric Moulines; Karim Abed-Meraim; Pascal Chevalier 001; Pierre Duhamel (IEEE Trans. Signal Process., 2001)

Semidefinite positive relaxation of the maximum-likelihood criterion applied to multiuser detection in a CDMA context. - Moussa Abdi; Hassan El Nahas; Alexandre Jard; Eric Moulines (IEEE Signal Process. Lett., 2002)

Long-range dependence and heavy-tail modeling for teletraffic data. - Olivier Cappé; Eric Moulines; Jean-Christophe Pesquet; Athina P. Petropulu; Xueshi Yang (IEEE Signal Process. Mag., 2002)

A semi-blind channel estimation technique based on second-order blind method for CDMA systems. - Samson Lasaulce; Philippe Loubaton; Eric Moulines (IEEE Trans. Signal Process., 2003)

Global Sampling for Sequential Filtering over Discrete State Space. - Pascal Cheung-Mon-Chan; Eric Moulines (EURASIP J. Adv. Signal Process., 2004)

Inference in hidden Markov models. - Olivier Cappé; Eric Moulines; Tobias Rydén (Springer, 2005)

Méthodologie de sélection de caractéristiques pour la classification d'images satellitaires. - Marine Campedel; Eric Moulines (CAP, 2005)

On the use of particle filtering for maximum likelihood parameter estimation. - Olivier Cappé; Eric Moulines (EUSIPCO, 2005)

Modeling, identification, and control of large-scale dynamical systems. - Simon Haykin 001; Alfred O. Hero III; Eric Moulines (ICASSP (5), 2005)

Comparison of Resampling Schemes for Particle Filtering - Randal Douc; Olivier Cappé; Eric Moulines (CoRR, 2005)

Classification et sélection de caractéristiques de textures. Utilisation d'algorithmes automatiques supervisés de sélection d'attributs pour la classification d'images. - Marine Campedel; Eric Moulines (Rev. d'Intelligence Artif., 2005)

Stability of Stochastic Approximation under Verifiable Conditions. - Christophe Andrieu; Eric Moulines; Pierre Priouret (SIAM J. Control. Optim., 2005)

Recursive Em Algorithm with Applications to Doa Estimation. - Olivier Cappé; Maurice Charbit; Eric Moulines (ICASSP (3), 2006)

Source Localization from Quantized Time of Arrival Measurements. - Nadir Castañeda; Maurice Charbit; Eric Moulines (ICASSP (4), 2006)

Energy Spectrum Reconstruction for HPGe Detectors Using Analytical Pile-Up Correction. - Thomas Trigano; François Roueff; Eric Moulines; Antoine Souloumiac; Thierry Montagu (ICASSP (3), 2006)

ODE methods for Markov chain stability with applications to MCMC. - Gersende Fort; Eric Moulines; Sean P. Meyn; Pierre Priouret (VALUETOOLS, 2006)

A New Approach for Mobile Localization in Multipath Scenarios. - Nadir Castañeda; Maurice Charbit; Eric Moulines (ICC, 2007)

Testing for Homogeneity with Kernel Fisher Discriminant Analysis. - Zaïd Harchaoui; Francis R. Bach; Eric Moulines (NIPS, 2007)

Online EM Algorithm for Latent Data Models - Olivier Cappé; Eric Moulines (CoRR, 2007)

Optimization of Radio Measurements Exploitation in Wireless Mobile Networks. - Afef Ben Hadj Alaya-Feki; Alain Le Cornec; Eric Moulines (J. Commun., 2007)

An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo. - Olivier Cappé; Simon J. Godsill; Eric Moulines (Proc. IEEE, 2007)

Statistical Pileup Correction Method for HPGe Detectors. - Thomas Trigano; Antoine Souloumiac; Thierry Montagu; François Roueff; Eric Moulines (IEEE Trans. Signal Process., 2007)

A Near Optimal Policy for Channel Allocation in Cognitive Radio. - Sarah Filippi; Olivier Cappé; Fabrice Clérot; Eric Moulines (EWRL, 2008)

Opportunistic Spectrum Access: Online Search of Optimality. - Afef Ben Hadj Alaya-Feki; Berna Sayraç; Eric Moulines; Alain Le Cornec (GLOBECOM, 2008)

Kernel Change-point Analysis. - Zaïd Harchaoui; Francis R. Bach; Eric Moulines (NIPS, 2008)

Informed spectrum usage in cognitive radio networks: Interference cartography. - Afef Ben Hadj Alaya-Feki; Sana Ben Jemaa; Berna Sayraç; Paul Houzé; Eric Moulines (PIMRC, 2008)

Semi Dynamic Parameter Tuning for Optimized Opportunistic Spectrum Access. - Afef Ben Hadj Alaya-Feki; Berna Sayraç; Alain Le Cornec; Eric Moulines (VTC Fall, 2008)

Opportunistic Spectrum Access with IEEE 802.11 in IEEE P1900.4 Framework. - Afef Ben Hadj Alaya-Feki; Berna Sayraç; Paul Houzé; Eric Moulines (WiMob, 2008)

Adaptive methods for sequential importance sampling with application to state space models. - Julien Cornebise; Eric Moulines; Jimmy Olsson (Stat. Comput., 2008)

On the error exponents for detecting randomly sampled noisy diffusion processes. - Walid Hachem; Eric Moulines; Jamal Najim; François Roueff (ICASSP, 2009)

Error exponents for Neyman-Pearson detection of a continuous-time Gaussian Markov process from noisy irregular samples - Walid Hachem; Eric Moulines; François Roueff (CoRR, 2009)

On approximate maximum-likelihood methods for blind identification: how to cope with the curse of dimensionality. - Steffen Barembruch; Aurélien Garivier; Eric Moulines (IEEE Trans. Signal Process., 2009)

Maximum likelihood blind deconvolution for sparse systems. - Steffen Barembruch; Anna Scaglione; Eric Moulines (CIP, 2010)

Detecting aircraft with a low resolution infrared sensor. - Jérémie Jakubowicz; Sidonie Lefebvre; Eric Moulines (IGARSS, 2010)

High-Rate Quantization for the Neyman-Pearson Detection of Hidden Markov Processes - Joffrey Villard; Pascal Bianchi; Eric Moulines; Pablo Piantanida (CoRR, 2010)

The expectation and sparse maximization algorithm. - Steffen Barembruch; Anna Scaglione; Eric Moulines (J. Commun. Networks, 2010)

On Upper-Confidence Bound Policies for Switching Bandit Problems. - Aurélien Garivier; Eric Moulines (ALT, 2011)

An algorithm for fast REM construction. - Sebastien Grimoud; Berna Sayraç; Sana Ben Jemaa; Eric Moulines (CrownCom, 2011)

CLT for eigen-inference methods in cognitive radios. - Jianfeng Yao; Romain Couillet; Jamal Najim; Eric Moulines; Mérouane Debbah (ICASSP, 2011)

Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning. - Francis R. Bach; Eric Moulines (NIPS, 2011)

Best Sensor Selection for an Iterative REM Construction. - Sebastien Grimoud; Berna Sayraç; Sana Ben Jemaa; Eric Moulines (VTC Fall, 2011)

On adaptive stratification. - Pierre Etoré; Gersende Fort; Benjamin Jourdain; Eric Moulines (Ann. Oper. Res., 2011)

Error Exponents for Neyman-Pearson Detection of a Continuous-Time Gaussian Markov Process From Regular or Irregular Samples. - Walid Hachem; Eric Moulines; François Roueff (IEEE Trans. Inf. Theory, 2011)

Inference of a Generalized Long Memory Process in the Wavelet Domain. - Olaf Kouamo; Maurice Charbit; Eric Moulines; François Roueff (IEEE Trans. Signal Process., 2011)

Sequential design of computer experiments for parameter estimation with application to numerical dosimetry. - Marjorie Jala; Céline Lévy-Leduc; Eric Moulines; Emmanuelle Conil; Joe Wiart (EUSIPCO, 2012)

New Online EM Algorithms for General Hidden Markov Models. Application to the SLAM Problem. - Sylvain Le Corff; Gersende Fort; Eric Moulines (LVA/ICA, 2012)

An online learning algorithm for mixture models of deformable templates. - Florian Maire; Sidonie Lefebvre; Randal Douc; Eric Moulines (MLSP, 2012)

Improving coverage estimation for cellular networks with spatial bayesian prediction based on measurements. - Berna Sayraç; Janne Riihijärvi; Petri Mähönen; Sana Ben Jemaa; Eric Moulines; Sebastien Grimoud (CellNet@SIGCOMM, 2012)

Detecting Aircraft With a Low-Resolution Infrared Sensor. - Jérémie Jakubowicz; Sidonie Lefebvre; Florian Maire; Eric Moulines (IEEE Trans. Image Process., 2012)

Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n). - Francis R. Bach; Eric Moulines (NIPS, 2013)

Centralized self-optimization of pilot powers for load balancing in LTE. - Yasir Khan; Berna Sayraç; Eric Moulines (PIMRC, 2013)

Surrogate Based Centralized SON: Application to Interference Mitigation in LTE-A HetNets. - Yasir Khan; Berna Sayraç; Eric Moulines (VTC Spring, 2013)

Surrogate Based Centralized Automated Optimization Applied to LTE Mobility Load Balancing. - Yasir Khan; Berna Sayraç; Eric Moulines (VTC Fall, 2013)

Centralized self-optimization in LTE-A using Active Antenna Systems. - Yasir Khan; Berna Sayraç; Eric Moulines (Wireless Days, 2013)

Aircraft classification with a low resolution infrared sensor. - Sidonie Lefebvre; Stéphanie Allassonnière; Jérémie Jakubowicz; Thomas Lasne; Eric Moulines (Mach. Vis. Appl., 2013)

Kernel-Based Methods for Hypothesis Testing: A Unified View. - Zaïd Harchaoui; Francis R. Bach; Olivier Cappé; Eric Moulines (IEEE Signal Process. Mag., 2013)

Adaptive Equi-Energy Sampler: Convergence and Illustration. - Amandine Schreck; Gersende Fort; Eric Moulines (ACM Trans. Model. Comput. Simul., 2013)

Probabilistic low-rank matrix completion on finite alphabets. - Jean Lafond; Olga Klopp; Eric Moulines; Joseph Salmon (NIPS, 2014)

Coverage mapping using spatial interpolation with field measurements. - Hajer Braham; Sana Ben Jemaa; Berna Sayraç; Gersende Fort; Eric Moulines (PIMRC, 2014)

Active antenna systems for centralized self-optimization of capacity in LTE-A. - Yasir Khan; Berna Sayraç; Eric Moulines (WCNC Workshops, 2014)

Low complexity spatial interpolation for cellular coverage analysis. - Hajer Braham; Sana Ben Jemaa; Berna Sayraç; Gersende Fort; Eric Moulines (WiOpt, 2014)

Adaptive sequential Monte Carlo by means of mixture of experts. - Julien Cornebise; Eric Moulines; Jimmy Olsson (Stat. Comput., 2014)

Centralized self-optimization of interference management in LTE-A HetNets. - Yasir Khan; Berna Sayraç; Eric Moulines (Design and Deployment of Small Cell Networks, 2015)

The sexy job in the next ten years will be statisticians. - Eric Moulines (DSAA, 2015)

Convergence Analysis of a Stochastic Projection-free Algorithm. - Jean Lafond; Hoi-To Wai; Eric Moulines (CoRR, 2015)

Quantitative bounds of convergence for geometrically ergodic Markov chain in the Wasserstein distance with application to the Metropolis Adjusted Langevin Algorithm. - Alain Durmus; Eric Moulines (Stat. Comput., 2015)

On parallel implementation of sequential Monte Carlo methods: the island particle model. - Christelle Vergé; Cyrille Dubarry; Pierre Del Moral; Eric Moulines (Stat. Comput., 2015)

Maximum likelihood estimation of a low-order building model. - Tahar Nabil; Eric Moulines; François Roueff; Jean-Marc Jicquel; Alexandre Girard (EUSIPCO, 2016)

A projection-free decentralized algorithm for non-convex optimization. - Hoi-To Wai; Anna Scaglione; Jean Lafond; Eric Moulines (GlobalSIP, 2016)

D-FW: Communication efficient distributed algorithms for high-dimensional sparse optimization. - Jean Lafond; Hoi-To Wai; Eric Moulines (ICASSP, 2016)

On the LP-convergence of a Girsanov theorem based particle filter. - Simo Särkkä; Eric Moulines (ICASSP, 2016)

Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo. - Alain Durmus; Umut Simsekli; Eric Moulines; Roland Badeau; Gaël Richard (NIPS, 2016)

Nonparametric estimation of a shot-noise process. - Paul Ilhe; François Roueff; Eric Moulines; Antoine Souloumiac (SSP, 2016)

Considering spatial information to improve anomaly detection in heterogeneous hyperspectral images. - François Weber; Sidonie Lefebvre; Eric Moulines; Marc Bousquet; Nicolas Roux (WHISPERS, 2016)

Decentralized Projection-free Optimization for Convex and Non-convex Problems. - Hoi-To Wai; Jean Lafond; Anna Scaglione; Eric Moulines (CoRR, 2016)

A Shrinkage-Thresholding Metropolis Adjusted Langevin Algorithm for Bayesian Variable Selection. - Amandine Schreck; Gersende Fort; Sylvain Le Corff; Eric Moulines (IEEE J. Sel. Top. Signal Process., 2016)

Convergence of Markovian Stochastic Approximation with Discontinuous Dynamics. - Gersende Fort; Eric Moulines; Amandine Schreck; Matti Vihola (SIAM J. Control. Optim., 2016)

Sequential Design of Computer Experiments for the Assessment of Fetus Exposure to Electromagnetic Fields. - Marjorie Jala; Céline Lévy-Leduc; Eric Moulines; Emmanuelle Conil; Joe Wiart (Technometrics, 2016)

Spatial Prediction Under Location Uncertainty in Cellular Networks. - Hajer Braham; Sana Ben Jemaa; Gersende Fort; Eric Moulines; Berna Sayraç (IEEE Trans. Wirel. Commun., 2016)

Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo. - Nicolas Brosse; Alain Durmus; Eric Moulines; Marcelo Pereyra (COLT, 2017)

Parallelized Stochastic Gradient Markov Chain Monte Carlo algorithms for non-negative matrix factorization. - Umut Simsekli; Alain Durmus; Roland Badeau; Gaël Richard; Eric Moulines; A. Taylan Cemgil (ICASSP, 2017)

Fast and privacy preserving distributed low-rank regression. - Hoi-To Wai; Anna Scaglione; Jean Lafond; Eric Moulines (ICASSP, 2017)

Online EM for functional data. - Florian Maire; Eric Moulines; Sidonie Lefebvre (Comput. Stat. Data Anal., 2017)

Particle rejuvenation of Rao-Blackwellized sequential Monte Carlo smoothers for conditionally linear and Gaussian models. - Ngoc Minh Nguyen; Sylvain Le Corff; Eric Moulines (EURASIP J. Adv. Signal Process., 2017)

Optimal scaling of the random walk Metropolis algorithm under L p mean differentiability. - Alain Durmus; Sylvain Le Corff; Eric Moulines; Gareth O. Roberts (J. Appl. Probab., 2017)

On Perturbed Proximal Gradient Algorithms. - Yves F. Atchadé; Gersende Fort; Eric Moulines (J. Mach. Learn. Res., 2017)

MCMC design-based non-parametric regression for rare event. Application to nested risk computations. - Gersende Fort; Emmanuel Gobet; Eric Moulines (Monte Carlo Methods Appl., 2017)

Decentralized Frank-Wolfe Algorithm for Convex and Nonconvex Problems. - Hoi-To Wai; Jean Lafond; Anna Scaglione; Eric Moulines (IEEE Trans. Autom. Control., 2017)

Fixed Rank Kriging for Cellular Coverage Analysis. - Hajer Braham; Sana Ben Jemaa; Gersende Fort; Eric Moulines; Berna Sayraç (IEEE Trans. Veh. Technol., 2017)

Bounds on the Covariance Matrix of a Class of Kalman-Bucy Filters for Systems with Non-Linear Dynamics. - Toni Karvonen; Silvère Bonnabel; Eric Moulines; Simo Särkkä (CDC, 2018)

Optimizing Thermal Comfort and Energy Consumption in a Large Building without Renovation Work. - Sylvain Le Corff; Alain Champagne; Maurice Charbit; Gilles Nozière; Eric Moulines (DSW, 2018)

The promises and pitfalls of Stochastic Gradient Langevin Dynamics. - Nicolas Brosse; Alain Durmus; Eric Moulines (NeurIPS, 2018)

Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames. - Geneviève Robin; Hoi-To Wai; Julie Josse; Olga Klopp; Eric Moulines (NeurIPS, 2018)

Stochastic Fista Algorithms: So Fast ? - Gersende Fort; Laurent Risser; Yves F. Atchadé; Eric Moulines (SSP, 2018)

MONK - Outlier-Robust Mean Embedding Estimation by Median-of-Means. - Matthieu Lerasle; Zoltán Szabó; Guillaume Lecué; Gaspar Massiot; Eric Moulines (CoRR, 2018)

Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau. - Alain Durmus; Eric Moulines; Marcelo Pereyra (SIAM J. Imaging Sci., 2018)

Non-asymptotic Analysis of Biased Stochastic Approximation Scheme. - Belhal Karimi; Blazej Miasojedow; Eric Moulines; Hoi-To Wai (COLT, 2019)

On the Global Convergence of (Fast) Incremental Expectation Maximization Methods. - Belhal Karimi; Hoi-To Wai; Eric Moulines; Marc Lavielle (NeurIPS, 2019)

Low-rank model with covariates for count data with missing values. - Geneviève Robin; Julie Josse; Eric Moulines; Sylvain Sardy (J. Multivar. Anal., 2019)

Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise. - Maxim Kaledin; Eric Moulines; Alexey Naumov; Vladislav Tadic; Hoi-To Wai (COLT, 2020)

Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations. - Robert Mattila; Cristian R. Rojas; Eric Moulines; Vikram Krishnamurthy; Bo Wahlberg (ICML, 2020)

A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm. - Gersende Fort; Eric Moulines; Hoi-To Wai (NeurIPS, 2020)

On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation. - Nicolas Brosse; Carlos Riquelme; Alice Martin 001; Sylvain Gelly; Eric Moulines (CoRR, 2020)

MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference. - Achille Thin; Nikita Kotelevskii; Jean-Stanislas Denain; Léo Grinsztajn; Alain Durmus; Maxim Panov; Eric Moulines (CoRR, 2020)

Convergence Analysis of Riemannian Stochastic Approximation Schemes. - Alain Durmus; Pablo Jiménez; Éric Moulines; Salem Said; Hoi-To Wai (CoRR, 2020)

A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm. - Gersende Fort; Eric Moulines; Hoi-To Wai (CoRR, 2020)

f-SAEM: A fast stochastic approximation of the EM algorithm for nonlinear mixed effects models. - Belhal Karimi; Marc Lavielle; Eric Moulines (Comput. Stat. Data Anal., 2020)

Variance reduction for Markov chains with application to MCMC. - Denis Belomestny; Leonid Iosipoi; Eric Moulines; Alexey Naumov; Sergey Samsonov (Stat. Comput., 2020)

On Stability of a Class of Filters for Nonlinear Stochastic Systems. - Toni Karvonen; Silvère Bonnabel; Eric Moulines; Simo Särkkä (SIAM J. Control. Optim., 2020)

On Riemannian Stochastic Approximation Schemes with Fixed Step-Size. - Alain Durmus; Pablo Jiménez; Eric Moulines; Salem Said (AISTATS, 2021)

On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning. - Alain Durmus; Eric Moulines; Alexey Naumov; Sergey Samsonov; Hoi-To Wai (COLT, 2021)

Geom-Spider-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization. - Gersende Fort; Eric Moulines; Hoi-To Wai (ICASSP, 2021)

Counterfactual Credit Assignment in Model-Free Reinforcement Learning. - Thomas Mesnard; Theophane Weber; Fabio Viola; Shantanu Thakoor; Alaa Saade; Anna Harutyunyan; Will Dabney; Thomas S. Stepleton; Nicolas Heess; Arthur Guez; Eric Moulines; Marcus Hutter; et al. (ICML, 2021)

DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs. - Vincent Plassier; Maxime Vono; Alain Durmus; Eric Moulines (ICML, 2021)

Monte Carlo Variational Auto-Encoders. - Achille Thin; Nikita Kotelevskii; Arnaud Doucet; Alain Durmus; Eric Moulines; Maxim Panov (ICML, 2021)

Federated-EM with heterogeneity mitigation and variance reduction. - Aymeric Dieuleveut; Gersende Fort; Eric Moulines; Geneviève Robin (NeurIPS, 2021)

Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize. - Alain Durmus; Eric Moulines; Alexey Naumov; Sergey Samsonov; Kevin Scaman; Hoi-To Wai (NeurIPS, 2021)

NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform. - Achille Thin; Yazid Janati El Idrissi; Sylvain Le Corff; Charles Ollion; Eric Moulines; Arnaud Doucet; Alain Durmus; Christian X. Robert (NeurIPS, 2021)

The Perturbed Prox-Preconditioned Spider Algorithm: Non-Asymptotic Convergence Bounds. - Gersende Fort; Eric Moulines (SSP, 2021)

The Perturbed Prox-Preconditioned Spider Algorithm for EM-Based Large Scale Learning. - Gersende Fort; Eric Moulines (SSP, 2021)

Model-free policy evaluation in Reinforcement Learning via upper solutions. - Denis Belomestny; Ilya Levin; Eric Moulines; Alexey Naumov; Sergey Samsonov; Veronika Zorina (CoRR, 2021)

DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm. - Vincent Plassier; Maxime Vono; Alain Durmus; Eric Moulines (CoRR, 2021)

Uniform minorization condition and convergence bounds for discretizations of kinetic Langevin dynamics. - Alain Durmus; Aurélien Enfroy; Éric Moulines; Gabriel Stoltz (CoRR, 2021)

Federated Expectation Maximization with heterogeneity mitigation and variance reduction. - Aymeric Dieuleveut; Gersende Fort; Eric Moulines; Geneviève Robin (CoRR, 2021)

Ex2MCMC: Sampling through Exploration Exploitation. - Evgeny Lagutin; Daniil Selikhanovych; Achille Thin; Sergey Samsonov; Alexey Naumov; Denis Belomestny; Maxim Panov; Eric Moulines (CoRR, 2021)

Variance Reduction for Dependent Sequences with Applications to Stochastic Gradient MCMC. - Denis Belomestny; Leonid Iosipoi; Eric Moulines; Alexey Naumov; Sergey Samsonov (SIAM/ASA J. Uncertain. Quantification, 2021)

Fast incremental expectation maximization for finite-sum optimization: nonasymptotic convergence. - Gersende Fort; P. Gach; Eric Moulines (Stat. Comput., 2021)

On Stochastic Gradient Langevin Dynamics with Dependent Data Streams: The Fully Nonconvex Case. - Ngoc Huy Chau; Éric Moulines; Miklós Rásonyi; Sotirios Sabanis; Ying Zhang 037 (SIAM J. Math. Data Sci., 2021)

QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. - Maxime Vono; Vincent Plassier; Alain Durmus; Aymeric Dieuleveut; Eric Moulines (AISTATS, 2022)

Minimization by Incremental Stochastic Surrogate Optimization for Large Scale Nonconvex Problems. - Belhal Karimi; Hoi-To Wai; Eric Moulines; Ping Li 001 (ALT, 2022)

A Patient-Specific Single Equivalent Dipole Model. - Gabriel Cardoso 001; Geneviève Robin; Andony Arrieula; Mark Potse; Michel Haïssaguerre; Eric Moulines; Rémi Dubois (CinC, 2022)

Diffusion bridges vector quantized variational autoencoders. - Max Cohen; Guillaume Quispe; Sylvain Le Corff; Charles Ollion; Eric Moulines (ICML, 2022)

From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. - Daniil Tiapkin; Denis Belomestny; Eric Moulines; Alexey Naumov; Sergey Samsonov; Yunhao Tang; Michal Valko; Pierre Ménard (ICML, 2022)

BR-SNIS: Bias Reduced Self-Normalized Importance Sampling. - Gabriel Cardoso 001; Sergey Samsonov; Achille Thin; Eric Moulines; Jimmy Olsson (NeurIPS, 2022)

FedPop: A Bayesian Approach for Personalised Federated Learning. - Nikita Kotelevskii; Maxime Vono; Alain Durmus; Eric Moulines (NeurIPS, 2022)

Local-Global MCMC kernels: the best of both worlds. - Sergey Samsonov; Evgeny Lagutin; Marylou Gabrié; Alain Durmus; Alexey Naumov; Eric Moulines (NeurIPS, 2022)

Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Rémi Munos; Alexey Naumov; Mark Rowland 001; Michal Valko; Pierre Ménard (NeurIPS, 2022)

On the geometric convergence for MALA under verifiable conditions. - Alain Durmus; Éric Moulines (CoRR, 2022)

Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study. - Tom Huix; Szymon Majewski; Alain Durmus; Eric Moulines; Anna Korba (CoRR, 2022)

Variance reduction for additive functionals of Markov chains via martingale representations. - Denis Belomestny; Eric Moulines; Sergey Samsonov (Stat. Comput., 2022)

A Proximal Markov Chain Monte Carlo Method for Bayesian Inference in Imaging Inverse Problems: When Langevin Meets Moreau. - Alain Durmus; Éric Moulines; Marcelo Pereyra (SIAM Rev., 2022)

ASkewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks. - Louis Leconte; Sholom Schechtman; Eric Moulines (AISTATS, 2023)

Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. - Vincent Plassier; Eric Moulines; Alain Durmus (AISTATS, 2023)

Restarted Bayesian Online Change-point Detection for Non-Stationary Markov Decision Processes. - Réda Alami; Mohammed Mahfoud; Eric Moulines (CoLLAs, 2023)

Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference. - Arnaud Descours; Tom Huix; Arnaud Guillin; Manon Michel; Éric Moulines; Boris Nectoux (COLT, 2023)

Orthogonal Directions Constrained Gradient Method: from non-linear equality constraints to Stiefel manifold. - Sholom Schechtman; Daniil Tiapkin; Michael Muehlebach; Éric Moulines (COLT, 2023)

Federated Boolean Neural Networks Learning. - Louis Leconte; Van Minh Nguyen; Eric Moulines (FMEC, 2023)

State and parameter learning with PARIS particle Gibbs. - Gabriel Cardoso 001; Yazid Janati El Idrissi; Sylvain Le Corff; Eric Moulines; Jimmy Olsson (ICML, 2023)

On Sampling with Approximate Transport Maps. - Louis Grenioux; Alain Oliviero Durmus; Eric Moulines; Marylou Gabrié (ICML, 2023)

Quantile Credit Assignment. - Thomas Mesnard; Wenqi Chen; Alaa Saade; Yunhao Tang; Mark Rowland 001; Theophane Weber; Clare Lyle; Audrunas Gruslys; Michal Valko; Will Dabney; Georg Ostrovski; Eric Moulines; et al. (ICML, 2023)

Conformal Prediction for Federated Uncertainty Quantification Under Label Shift. - Vincent Plassier; Mehdi Makni; Aleksandr Rubashevskii; Eric Moulines; Maxim Panov (ICML, 2023)

Fast Rates for Maximum Entropy Exploration. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Rémi Munos; Alexey Naumov; Pierre Perrault; Yunhao Tang; Michal Valko; Pierre Ménard (ICML, 2023)

Oil and Gas Automatic Infrastructure Mapping: Leveraging High-Resolution Satellite Imagery Through Fine-Tuning of Object Detection Models. - Jade Eva Guisiano; Éric Moulines; Thomas Lauvaux; Jérémie Sublime (ICONIP (12), 2023)

First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities. - Aleksandr Beznosikov; Sergey Samsonov; Marina Sheshukova; Alexander V. Gasnikov; Alexey Naumov; Eric Moulines (NeurIPS, 2023)

Model-free Posterior Sampling via Learning Rate Randomization. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Rémi Munos; Alexey Naumov; Pierre Perrault; Michal Valko; Pierre Ménard (NeurIPS, 2023)

FAVAS: Federated AVeraging with ASynchronous clients. - Louis Leconte; Van Minh Nguyen; Eric Moulines (CoRR, 2023)

Balanced Training of Energy-Based Models with Adaptive Flow Sampling. - Louis Grenioux; Éric Moulines; Marylou Gabrié (CoRR, 2023)

Monte Carlo guided Diffusion for Bayesian linear inverse problems. - Gabriel Cardoso 001; Yazid Janati El Idrissi; Sylvain Le Corff; Eric Moulines (CoRR, 2023)

Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study. - Fouzi Boukhalfa; Réda Alami; Mastane Achab; Eric Moulines; Mehdi Bennis (CoRR, 2023)

Finite-Sample Analysis of the Temporal Difference Learning. - Sergey Samsonov; Daniil Tiapkin; Alexey Naumov; Eric Moulines (CoRR, 2023)

One-Step Distributional Reinforcement Learning. - Mastane Achab; Réda Alami; Yasser Abdelaziz Dahou Djilali; Kirill Fedyanin; Eric Moulines (Trans. Mach. Learn. Res., 2023)

Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning. - Aymeric Dieuleveut; Gersende Fort; Eric Moulines; Hoi-To Wai (IEEE Trans. Signal Process., 2023)

Unifying mirror descent and dual averaging. - Anatoli B. Juditsky; Joon Kwon; Éric Moulines (Math. Program., 2023)

Stochastic variable metric proximal gradient with variance reduction for non-convex composite optimization. - Gersende Fort; Eric Moulines (Stat. Comput., 2023)

Queuing dynamics of asynchronous Federated Learning. - Louis Leconte; Matthieu Jonckheere; Sergey Samsonov; Eric Moulines (AISTATS, 2024)

Efficient Conformal Prediction under Data Heterogeneity. - Vincent Plassier; Nikita Kotelevskii; Aleksandr Rubashevskii; Fedor Noskov; Maksim Velikanov; Alexander Fishkov; Samuel Horváth; Martin Takác 001; Eric Moulines; Maxim Panov (AISTATS, 2024)

Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability. - Sergey Samsonov; Daniil Tiapkin; Alexey Naumov; Eric Moulines (COLT, 2024)

FAVANO: Federated Averaging with Asynchronous Nodes. - Louis Leconte; Van Minh Nguyen; Eric Moulines (ICASSP, 2024)

Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study. - Fouzi Boukhalfa; Réda Alami; Mastane Achab; Eric Moulines; Mehdi Bennis; Thierry Lestable (ICC Workshops, 2024)

Monte Carlo guided Denoising Diffusion models for Bayesian linear inverse problems. - Gabriel Cardoso 001; Yazid Janati El Idrissi; Sylvain Le Corff; Eric Moulines (ICLR, 2024)

Demonstration-Regularized RL. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Alexey Naumov; Pierre Perrault; Michal Valko; Pierre Ménard (ICLR, 2024)

Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians. - Tom Huix; Anna Korba; Alain Oliviero Durmus; Eric Moulines (ICML, 2024)

Incentivized Learning in Principal-Agent Bandit Games. - Antoine Scheid; Daniil Tiapkin; Etienne Boursier; Aymeric Capitaine; Eric Moulines; Michael I. Jordan; El-Mahdi El-Mhamdi; Alain Oliviero Durmus (ICML, 2024)

Object Detection Models Sensitivity & Robustness to Satellite-based Adversarial Attacks. - Jade Eva Guisiano; Domenico Barretta; Éric Moulines; Thomas Lauvaux; Jérémie Sublime (IGARSS, 2024)

Leveraging an ECG Beat Diffusion Model for Morphological Reconstruction from Indirect Signals. - Lisa Bedin; Gabriel Cardoso 001; Josselin Duchateau; Rémi Dubois; Eric Moulines (NeurIPS, 2024)

Piecewise deterministic generative models. - Andrea Bertazzi; Dario Shariatian; Umut Simsekli; Eric Moulines; Alain Durmus (NeurIPS, 2024)

Unravelling in Collaborative Learning. - Aymeric Capitaine; Etienne Boursier; Antoine Scheid; Eric Moulines; Michael I. Jordan; El-Mahdi El-Mhamdi; Alain Durmus (NeurIPS, 2024)

Divide-and-Conquer Posterior Sampling for Denoising Diffusion priors. - Yazid Janati; Badr Moufad; Alain Durmus; Eric Moulines; Jimmy Olsson (NeurIPS, 2024)

SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning. - Paul Mangold; Sergey Samsonov; Safwan Labbi; Ilya Levin; Réda Alami; Alexey Naumov; Eric Moulines (NeurIPS, 2024)

Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning. - Sergey Samsonov; Eric Moulines; Qi-Man Shao; Zhuo-Song Zhang; Alexey Naumov (NeurIPS, 2024)

Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality. - Antoine Scheid; Aymeric Capitaine; Etienne Boursier; Eric Moulines; Michael I. Jordan; Alain Durmus (NeurIPS, 2024)

Bayesian ECG reconstruction using denoising diffusion generative models. - Gabriel Victorino Cardoso; Lisa Bedin; Josselin Duchateau; Rémi Dubois; Eric Moulines (CoRR, 2024)

SCAFFLSA: Quantifying and Eliminating Heterogeneity Bias in Federated Linear Stochastic Approximation and Temporal Difference Learning. - Paul Mangold; Sergey Samsonov; Safwan Labbi; Ilya Levin; Réda Alami; Alexey Naumov; Eric Moulines (CoRR, 2024)

Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors. - Yazid Janati El Idrissi; Alain Durmus; Eric Moulines; Jimmy Olsson (CoRR, 2024)

ReALLM: A general framework for LLM compression and fine-tuning. - Louis Leconte; Lisa Bedin; Van Minh Nguyen; Eric Moulines (CoRR, 2024)

Central Limit Theorem for Bayesian Neural Network trained with Variational Inference. - Arnaud Descours; Tom Huix; Arnaud Guillin; Manon Michel; Éric Moulines; Boris Nectoux (CoRR, 2024)

Mitigating Externalities while Learning: an Online Version of the Coase Theorem. - Antoine Scheid; Aymeric Capitaine; Etienne Boursier; Eric Moulines; Michael I. Jordan; Alain Durmus (CoRR, 2024)

Conditionally valid Probabilistic Conformal Prediction. - Vincent Plassier; Alexander Fishkov; Maxim Panov; Eric Moulines (CoRR, 2024)

A New Bound on the Cumulant Generating Function of Dirichlet Processes. - Pierre Perrault; Denis Belomestny; Pierre Ménard; Éric Moulines; Alexey Naumov; Daniil Tiapkin; Michal Valko (CoRR, 2024)

Optimal Design for Reward Modeling in RLHF. - Antoine Scheid; Etienne Boursier; Alain Durmus; Michael I. Jordan; Pierre Ménard; Eric Moulines; Michal Valko (CoRR, 2024)

Joint Channel Selection using FedDRL in V2X. - Lorenzo Mancini; Safwan Labbi; Karim Abed-Meraim; Fouzi Boukhalfa; Alain Durmus; Paul Mangold; Eric Moulines (CoRR, 2024)

Refined Analysis of Federated Averaging's Bias and Federated Richardson-Romberg Extrapolation. - Paul Mangold; Alain Durmus; Aymeric Dieuleveut; Sergey Samsonov; Eric Moulines (CoRR, 2024)

Rates of convergence for density estimation with generative adversarial networks. - Nikita Puchkin; Sergey Samsonov; Denis Belomestny; Eric Moulines; Alexey Naumov (J. Mach. Learn. Res., 2024)

Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents. - Safwan Labbi; Daniil Tiapkin; Lorenzo Mancini; Paul Mangold; Eric Moulines (AISTATS, 2025)

Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation. - Paul Mangold; Alain Oliviero Durmus; Aymeric Dieuleveut; Sergey Samsonov; Eric Moulines (AISTATS, 2025)

Atlas-Chat: Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect. - Guokan Shang; Hadi Abdine; Yousef Khoubrane; Amr Mohamed 005; Yassine Abbahaddou; Sofiane Ennadir; Imane Momayiz; Xuguang Ren; Eric Moulines; Preslav Nakov; Michalis Vazirgiannis; Eric P. Xing (COLING Workshops, 2025)

From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation. - Nikita Kotelevskii; Vladimir Kondratyev; Martin Takác 001; Eric Moulines; Maxim Panov (ICLR, 2025)

Variational Diffusion Posterior Sampling with Midpoint Guidance. - Badr Moufad; Yazid Janati; Lisa Bedin; Alain Oliviero Durmus; Randal Douc; Eric Moulines; Jimmy Olsson (ICLR, 2025)

Probabilistic Conformal Prediction with Approximate Conditional Validity. - Vincent Plassier; Alexander Fishkov; Mohsen Guizani; Maxim Panov; Eric Moulines (ICLR, 2025)

Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation. - Marina Sheshukova; Denis Belomestny; Alain Oliviero Durmus; Eric Moulines; Alexey Naumov; Sergey Samsonov (ICLR, 2025)

Health-aware optimal charging of lithium-ion batteries using deep-neural networks-based explicit constrained model predictive control. - Ahmed Shokry; Mehdi Abou El Qassime; Antonio Espuña; Eric Moulines (Comput. Chem. Eng., 2025)

A hybrid approach for steady-state production optimization of a real oil and gas platform: Integrating physics-based models, machine learning techniques, and field monitoring signals. - Luca Trevisan 003; Ahmed Shokry; Marco Montini; Eric Moulines; Enrico Zio (Comput. Chem. Eng., 2025)

Finite-Time High-Probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation. - Alain Durmus; Eric Moulines; Alexey Naumov; Sergey Samsonov (Math. Oper. Res., 2025)

Prediction-Aware Learning in Multi-Agent Systems. - Aymeric Capitaine; Etienne Boursier; Eric Moulines; Michael I. Jordan; Alain Durmus (CoRR, 2025)

Learning Contracts in Hierarchical Multi-Agent Systems. - Antoine Scheid; Etienne Boursier; Alain Durmus; Eric Moulines; Michael I. Jordan (CoRR, 2025)

A Mixture-Based Framework for Guiding Diffusion Models. - Yazid Janati; Badr Moufad; Mehdi Abou El Qassime; Alain Durmus; Eric Moulines; Jimmy Olsson (CoRR, 2025)

Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent. - Marina Sheshukova; Sergey Samsonov; Denis Belomestny; Eric Moulines; Qi-Man Shao; Zhuo-Song Zhang; Alexey Naumov (CoRR, 2025)

Optimizing Asynchronous Federated Learning: A Delicate Trade-Off Between Model-Parameter Staleness and Update Frequency. - Abdelkrim Alahyane; Céline Comte; Matthieu Jonckheere; Éric Moulines (CoRR, 2025)

Rectifying Conformity Scores for Better Conditional Coverage. - Vincent Plassier; Alexander Fishkov; Victor Dheur; Mohsen Guizani; Souhaib Ben Taieb; Maxim Panov; Eric Moulines (CoRR, 2025)

Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up. - Paul Mangold; Alain Durmus; Aymeric Dieuleveut; Eric Moulines (CoRR, 2025)

Online Decision-Focused Learning. - Aymeric Capitaine; Maxime Haddouche; Eric Moulines; Michael I. Jordan; Etienne Boursier; Alain Durmus (CoRR, 2025)

Adaptive Temperature Scaling with Conformal Prediction. - Nikita Kotelevskii; Mohsen Guizani; Eric Moulines; Maxim Panov (CoRR, 2025)

Statistical inference for Linear Stochastic Approximation with Markovian Noise. - Sergey Samsonov; Marina Sheshukova; Eric Moulines; Alexey Naumov (CoRR, 2025)

Accelerating Nash Learning from Human Feedback via Mirror Prox. - Daniil Tiapkin; Daniele Calandriello; Denis Belomestny; Eric Moulines; Alexey Naumov; Kashif Rasul; Michal Valko; Pierre Ménard (CoRR, 2025)

Conditional Diffusion Models with Classifier-Free Gibbs-like Guidance. - Badr Moufad; Yazid Janati; Alain Durmus; Ahmed Ghorbel; Eric Moulines; Jimmy Olsson (CoRR, 2025)

Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean-Field Games. - Antonio Ocello; Daniil Tiapkin; Lorenzo Mancini; Mathieu Laurière; Eric Moulines (CoRR, 2025)

On Global Convergence Rates for Federated Policy Gradient under Heterogeneous Environment. - Safwan Labbi; Paul Mangold; Daniil Tiapkin; Eric Moulines (CoRR, 2025)