Bayesian Reward Filtering. - Matthieu Geist; Olivier Pietquin; Gabriel Fricout (EWRL, 2008)

Kalman Temporal Differences: The deterministic case. - Matthieu Geist; Olivier Pietquin; Gabriel Fricout (ADPRL, 2009)

Kernelizing Vector Quantization Algorithms. - Matthieu Geist; Olivier Pietquin; Gabriel Fricout (ESANN, 2009)

Tracking in Reinforcement Learning. - Matthieu Geist; Olivier Pietquin; Gabriel Fricout (ICONIP (1), 2009)

Statistically linearized least-squares temporal differences. - Matthieu Geist; Olivier Pietquin (ICUMT, 2010)

Eligibility traces through colored noises. - Matthieu Geist; Olivier Pietquin (ICUMT, 2010)

Optimizing spoken dialogue management with fitted value iteration. - Senthilkumar Chandramohan; Matthieu Geist; Olivier Pietquin (INTERSPEECH, 2010)

Revisiting Natural Actor-Critics with Value Function Approximation. - Matthieu Geist; Olivier Pietquin (MDAI, 2010)

Sparse Approximate Dynamic Programming for Dialog Management. - Senthilkumar Chandramohan; Matthieu Geist; Olivier Pietquin (SIGDIAL Conference, 2010)

Kalman Temporal Differences. - Matthieu Geist; Olivier Pietquin (J. Artif. Intell. Res., 2010)

Différences temporelles de Kalman. Cas déterministe. - Matthieu Geist; Olivier Pietquin; Gabriel Fricout (Rev. d'Intelligence Artif., 2010)

Parametric value function approximation: A unified view. - Matthieu Geist; Olivier Pietquin (ADPRL, 2011)

Dynamic neural field optimization using the unscented Kalman filter. - Jérémy Fix; Matthieu Geist; Olivier Pietquin; Hervé Frezza-Buet (CCMB, 2011)

ℓ1-Penalized Projected Bellman Residual. - Matthieu Geist; Bruno Scherrer (EWRL, 2011)

Batch, Off-Policy and Model-Free Apprenticeship Learning. - Edouard Klein; Matthieu Geist; Olivier Pietquin (EWRL, 2011)

Recursive Least-Squares Learning with Eligibility Traces. - Bruno Scherrer; Matthieu Geist (EWRL, 2011)

Performance evaluation for particle filters. - Remi Chou; Yvo Boers; Martin Podt; Matthieu Geist (FUSION, 2011)

A Non-parametric Approach to Approximate Dynamic Programming. - Hadrien Glaude; Fadi Akrimi; Matthieu Geist; Olivier Pietquin (ICMLA (1), 2011)

Sample Efficient On-Line Learning of Optimal Dialogue Policies with Kalman Temporal Differences. - Olivier Pietquin; Matthieu Geist; Senthilkumar Chandramohan (IJCAI, 2011)

User Simulation in Dialogue Systems Using Inverse Reinforcement Learning. - Senthilkumar Chandramohan; Matthieu Geist; Fabrice Lefèvre; Olivier Pietquin (INTERSPEECH, 2011)

Uncertainty Management for On-Line Optimisation of a POMDP-Based Large-Scale Spoken Dialogue System. - Lucie Daubigney; Milica Gasic; Senthilkumar Chandramohan; Matthieu Geist; Olivier Pietquin; Steve J. Young (INTERSPEECH, 2011)

Optimization of a tutoring system from a fixed set of data. - Olivier Pietquin; Lucie Daubigney; Matthieu Geist (SLaTE, 2011)

Managing Uncertainty within KTD. - Matthieu Geist; Olivier Pietquin (Active Learning and Experimental Design @ AISTATS, 2011)

Sample-efficient batch reinforcement learning for dialogue management optimization. - Olivier Pietquin; Matthieu Geist; Senthilkumar Chandramohan; Hervé Frezza-Buet (ACM Trans. Speech Lang. Process., 2011)

Behavior Specific User Simulation in Spoken Dialogue Systems. - Senthilkumar Chandramohan; Matthieu Geist; Fabrice Lefèvre; Olivier Pietquin (ITG Conference on Speech Communication, 2012)

Monte-Carlo Swarm Policy Search. - Jérémy Fix; Matthieu Geist (ICAISC (SIDE-EC), 2012)

Clustering behaviors of Spoken Dialogue Systems users. - Senthilkumar Chandramohan; Matthieu Geist; Fabrice Lefèvre; Olivier Pietquin (ICASSP, 2012)

Off-policy learning in large-scale POMDP-based dialogue systems. - Lucie Daubigney; Matthieu Geist; Olivier Pietquin (ICASSP, 2012)

A Dantzig Selector Approach to Temporal Difference Learning. - Matthieu Geist; Bruno Scherrer; Alessandro Lazaric; Mohammad Ghavamzadeh (ICML, 2012)

Approximate Modified Policy Iteration. - Bruno Scherrer; Victor Gabillon; Mohammad Ghavamzadeh; Matthieu Geist (ICML, 2012)

Co-adaptation in Spoken Dialogue Systems. - Senthilkumar Chandramohan; Matthieu Geist; Fabrice Lefèvre; Olivier Pietquin (IWSDS, 2012)

Inverse Reinforcement Learning through Structured Classification. - Edouard Klein; Matthieu Geist; Bilal Piot; Olivier Pietquin (NIPS, 2012)

Optimisation d'un tuteur intelligent à partir d'un jeu de données fixé (Optimization of a tutoring system from a fixed set of data) [in French]. - Lucie Daubigney; Matthieu Geist; Olivier Pietquin (JEP-TALN-RECITAL 2012, 2012)

A Comprehensive Reinforcement Learning Framework for Dialogue Management Optimization. - Lucie Daubigney; Matthieu Geist; Senthilkumar Chandramohan; Olivier Pietquin (IEEE J. Sel. Top. Signal Process., 2012)

Laugh-aware virtual agent and its impact on user amusement. - Radoslaw Niewiadomski; Jennifer Hofmann; Jérôme Urbain; Tracey Platt; Johannes Wagner 001; Bilal Piot; Hüseyin Çakmak; Sathish Pammi; Tobias Baur 001; Stéphane Dupont; Matthieu Geist; Florian Lingenfelser; et al. (AAMAS, 2013)

Random projections: A remedy for overfitting issues in time series prediction with echo state networks. - Lucie Daubigney; Matthieu Geist; Olivier Pietquin (ICASSP, 2013)

Particle swarm optimisation of spoken dialogue system strategies. - Lucie Daubigney; Matthieu Geist; Olivier Pietquin (INTERSPEECH, 2013)

A Cascaded Supervised Learning Approach to Inverse Reinforcement Learning. - Edouard Klein; Bilal Piot; Matthieu Geist; Olivier Pietquin (ECML/PKDD (1), 2013)

Learning from Demonstrations: Is It Worth Estimating a Reward Function? - Bilal Piot; Matthieu Geist; Olivier Pietquin (ECML/PKDD (1), 2013)

Model-free POMDP optimisation of tutoring systems with echo-state networks. - Lucie Daubigney; Matthieu Geist; Olivier Pietquin (SIGDIAL Conference, 2013)

Policy Search: Any Local Optimum Enjoys a Global Performance Guarantee. - Bruno Scherrer; Matthieu Geist (CoRR, 2013)

A C++ template-based reinforcement learning library: fitting the code to the mathematics. - Hervé Frezza-Buet; Matthieu Geist (J. Mach. Learn. Res., 2013)

Classification structurée pour l'apprentissage par renforcement inverse. - Edouard Klein; Bilal Piot; Matthieu Geist; Olivier Pietquin (Rev. d'Intelligence Artif., 2013)

Algorithmic Survey of Parametric Value Function Approximation. - Matthieu Geist; Olivier Pietquin (IEEE Trans. Neural Networks Learn. Syst., 2013)

Boosted and reward-regularized classification for apprenticeship learning. - Bilal Piot; Matthieu Geist; Olivier Pietquin (AAMAS, 2014)

Predicting when to laugh with structured classification. - Bilal Piot; Olivier Pietquin; Matthieu Geist (INTERSPEECH, 2014)

Difference of Convex Functions Programming for Reinforcement Learning. - Bilal Piot; Matthieu Geist; Olivier Pietquin (NIPS, 2014)

Boosted Bellman Residual Minimization Handling Expert Demonstrations. - Bilal Piot; Matthieu Geist; Olivier Pietquin (ECML/PKDD (2), 2014)

Local Policy Search in a Convex Space and Conservative Policy Iteration as Boosted Policy Search. - Bruno Scherrer; Matthieu Geist (ECML/PKDD (3), 2014)

Off-policy learning with eligibility traces: a survey. - Matthieu Geist; Bruno Scherrer (J. Mach. Learn. Res., 2014)

Convolutional and Recurrent Neural Networks for Activity Recognition in Smart Environment. - Deepika Singh; Erinc Merdivan; Sten Hanke; Johannes Kropf; Matthieu Geist; Andreas Holzinger (BIRS-IMLKE, 2015)

Imitation Learning Applied to Embodied Conversational Agents. - Bilal Piot; Olivier Pietquin; Matthieu Geist (MLIS@ICML, 2015)

Inverse Reinforcement Learning in Relational Domains. - Thibaut Munzer; Bilal Piot; Matthieu Geist; Olivier Pietquin; Manuel Lopes 001 (IJCAI, 2015)

Approximate modified policy iteration and its application to the game of Tetris. - Bruno Scherrer; Mohammad Ghavamzadeh; Victor Gabillon; Boris Lesner; Matthieu Geist (J. Mach. Learn. Res., 2015)

Soft-max boosting. - Matthieu Geist (Mach. Learn., 2015)

Recherche locale de politique dans un espace convexe. - Bruno Scherrer; Matthieu Geist (Rev. d'Intelligence Artif., 2015)

Score-based Inverse Reinforcement Learning. - Layla El Asri; Bilal Piot; Matthieu Geist; Romain Laroche; Olivier Pietquin (AAMAS, 2016)

Softened Approximate Policy Iteration for Markov Games. - Julien Pérolat; Bilal Piot; Matthieu Geist; Bruno Scherrer; Olivier Pietquin (ICML, 2016)

Should one minimize the expected Bellman residual or maximize the mean value? - Matthieu Geist; Bilal Piot; Olivier Pietquin (CoRR, 2016)

Difference of Convex Functions Programming Applied to Control with Expert Data. - Bilal Piot; Matthieu Geist; Olivier Pietquin (CoRR, 2016)

Human Activity Recognition Using Recurrent Neural Networks. - Deepika Singh; Erinc Merdivan; Ismini Psychoula; Johannes Kropf; Sten Hanke; Matthieu Geist; Andreas Holzinger (CD-MAKE, 2017)

Is the Bellman residual a bad proxy? - Matthieu Geist; Bilal Piot; Olivier Pietquin (NIPS, 2017)

Reconstruct & Crush Network. - Erinc Merdivan; Mohammad Reza Loghmani; Matthieu Geist (NIPS, 2017)

Bridging the Gap Between Imitation Learning and Inverse Reinforcement Learning. - Bilal Piot; Matthieu Geist; Olivier Pietquin (IEEE Trans. Neural Networks Learn. Syst., 2017)

A Deep Learning Approach for Privacy Preservation in Assisted Living. - Ismini Psychoula; Erinc Merdivan; Deepika Singh; Liming Chen 001; Feng Chen 004; Sten Hanke; Johannes Kropf; Andreas Holzinger; Matthieu Geist (PerCom Workshops, 2018)

Deep Representation Learning for Domain Adaptation of Semantic Image Segmentation. - Assia Benbihi; Matthieu Geist; Cédric Pradalier (CoRR, 2018)

Anderson Acceleration for Reinforcement Learning. - Matthieu Geist; Bruno Scherrer (CoRR, 2018)

Image-based Natural Language Understanding Using 2D Convolutional Neural Networks. - Erinc Merdivan; Anastasios Vafeiadis; Dimitrios Kalatzis; Sten Hanke; Johannes Kropf; Konstantinos Votis; Dimitrios Giakoumis; Dimitrios Tzovaras; Liming Chen 001; Raouf Hamzaoui; Matthieu Geist (CoRR, 2018)

Deep Reinforcement Learning-based Continuous Control for Multicopter Systems. - Anush Manukyan; Miguel A. Olivares-Méndez; Matthieu Geist; Holger Voos (CoDIT, 2019)

Importance Sampling for Deep System Identification. - Antoine Mahé; Antoine Richard; Benjamin Mouscadet; Cédric Pradalier; Matthieu Geist (ICAR, 2019)

ELF: Embedded Localisation of Features in Pre-Trained CNN. - Assia Benbihi; Matthieu Geist; Cédric Pradalier (ICCV, 2019)

A Theory of Regularized Markov Decision Processes. - Matthieu Geist; Bruno Scherrer; Olivier Pietquin (ICML, 2019)

Learning from a Learner. - Alexis Jacq; Matthieu Geist; Ana Paiva 001; Olivier Pietquin (ICML, 2019)

Semi-supervised Domain Adaptation with Representation Learning for Semantic Segmentation Across Time. - Assia Benbihi; Matthieu Geist; Cédric Pradalier (ICONIP (5), 2019)

Learning Sensor Placement from Demonstration for UAV networks. - Assia Benbihi; Matthieu Geist; Cédric Pradalier (ISCC, 2019)

Image-Based Text Classification using 2D Convolutional Neural Networks. - Erinc Merdivan; Anastasios Vafeiadis; Dimitrios Kalatzis; Sten Hanke; Joahannes Kroph; Konstantinos Votis; Dimitrios Giakoumis; Dimitrios Tzovaras; Liming Chen 001; Raouf Hamzaoui; Matthieu Geist (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI, 2019)

Targeted Attacks on Deep Reinforcement Learning Agents through Adversarial Observations. - Léonard Hussenot; Matthieu Geist; Olivier Pietquin (CoRR, 2019)

MULEX: Disentangling Exploitation from Exploration in Deep RL. - Lucas Beyer; Damien Vincent; Olivier Teboul; Sylvain Gelly; Matthieu Geist; Olivier Pietquin (CoRR, 2019)

Approximate Fictitious Play for Mean Field Games. - Romuald Elie; Julien Pérolat; Mathieu Laurière; Matthieu Geist; Olivier Pietquin (CoRR, 2019)

Credit Assignment as a Proxy for Transfer in Reinforcement Learning. - Johan Ferret; Raphaël Marinier; Matthieu Geist; Olivier Pietquin (CoRR, 2019)

On Connections between Constrained Optimization and Reinforcement Learning. - Nino Vieillard; Olivier Pietquin; Matthieu Geist (CoRR, 2019)

Image-Based Place Recognition on Bucolic Environment Across Seasons From Semantic Edge Description. - Assia Benbihi; Matthieu Geist; Cédric Pradalier (CoRR, 2019)

Stable and Efficient Policy Evaluation. - Daoming Lyu; Bo Liu 006; Matthieu Geist; Wen Dong 001; Saad Biaz; Qi Wang 009 (IEEE Trans. Neural Networks Learn. Syst., 2019)

On the Convergence of Model Free Learning in Mean Field Games. - Romuald Elie; Julien Pérolat; Mathieu Laurière; Matthieu Geist; Olivier Pietquin (AAAI, 2020)

Deep Conservative Policy Iteration. - Nino Vieillard; Olivier Pietquin; Matthieu Geist (AAAI, 2020)

Foolproof Cooperative Learning. - Alexis Jacq; Julien Pérolat; Matthieu Geist; Olivier Pietquin (ACML, 2020)

Momentum in Reinforcement Learning. - Nino Vieillard; Bruno Scherrer; Olivier Pietquin; Matthieu Geist (AISTATS, 2020)

CopyCAT: : Taking Control of Neural Policies with Constant Attacks. - Léonard Hussenot; Matthieu Geist; Olivier Pietquin (AAMAS, 2020)

Modified Actor-Critics. - Erinc Merdivan; Sten Hanke; Matthieu Geist (AAMAS, 2020)

Image-Based Place Recognition on Bucolic Environment Across Seasons From Semantic Edge Description. - Assia Benbihi; Stéphanie Arravechia; Matthieu Geist; Cédric Pradalier (ICRA, 2020)

Self-Attentional Credit Assignment for Transfer in Reinforcement Learning. - Johan Ferret; Raphaël Marinier; Matthieu Geist; Olivier Pietquin (IJCAI, 2020)

Monte-Carlo Localization on Metal Plates Based on Ultrasonic Guided Waves. - Othmane-Latif Ouabi; Pascal Pomarede; Matthieu Geist; Nico F. Declercq; Cédric Pradalier (ISER, 2020)

Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications. - Sarah Perrin; Julien Pérolat; Mathieu Laurière; Matthieu Geist; Romuald Elie; Olivier Pietquin (NeurIPS, 2020)

Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning. - Nino Vieillard; Tadashi Kozuno; Bruno Scherrer; Olivier Pietquin; Rémi Munos; Matthieu Geist (NeurIPS, 2020)

Munchausen Reinforcement Learning. - Nino Vieillard; Olivier Pietquin; Matthieu Geist (NeurIPS, 2020)

Filling Gaps in Micro-meteorological Data. - Antoine Richard; Lior Fine; Offer Rozenstein; Josef Tanny; Matthieu Geist; Cédric Pradalier (ECML/PKDD (5), 2020)

Leverage the Average: an Analysis of Regularization in RL. - Nino Vieillard; Tadashi Kozuno; Bruno Scherrer; Olivier Pietquin; Rémi Munos; Matthieu Geist (CoRR, 2020)

What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study. - Marcin Andrychowicz; Anton Raichuk; Piotr Stanczyk; Manu Orsini; Sertan Girgin; Raphaël Marinier; Léonard Hussenot; Matthieu Geist; Olivier Pietquin; Marcin Michalski; Sylvain Gelly; Olivier Bachem (CoRR, 2020)

Self-Imitation Advantage Learning. - Johan Ferret; Olivier Pietquin; Matthieu Geist (AAMAS, 2021)

Show Me the Way: Intrinsic Motivation from Demonstrations. - Léonard Hussenot; Robert Dadashi; Matthieu Geist; Olivier Pietquin (AAMAS, 2021)

Learning Behaviors through Physics-driven Latent Imagination. - Antoine Richard; Stéphanie Aravecchia; Matthieu Geist; Cédric Pradalier (CoRL, 2021)

What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study. - Marcin Andrychowicz; Anton Raichuk; Piotr Stanczyk; Manu Orsini; Sertan Girgin; Raphaël Marinier; Léonard Hussenot; Matthieu Geist; Olivier Pietquin; Marcin Michalski; Sylvain Gelly; Olivier Bachem (ICLR, 2021)

Primal Wasserstein Imitation Learning. - Robert Dadashi; Léonard Hussenot; Matthieu Geist; Olivier Pietquin (ICLR, 2021)

Adversarially Guided Actor-Critic. - Yannis Flet-Berliac; Johan Ferret; Olivier Pietquin; Philippe Preux; Matthieu Geist (ICLR, 2021)

Offline Reinforcement Learning with Pseudometric Learning. - Robert Dadashi; Shideh Rezaeifar; Nino Vieillard; Léonard Hussenot; Olivier Pietquin; Matthieu Geist (ICML, 2021)

Hyperparameter Selection for Imitation Learning. - Léonard Hussenot; Marcin Andrychowicz; Damien Vincent; Robert Dadashi; Anton Raichuk; Sabela Ramos; Nikola Momchev; Sertan Girgin; Raphaël Marinier; Lukasz Stafiniak; Manu Orsini; Olivier Bachem; et al. (ICML, 2021)

Mean Field Games Flock! The Reinforcement Learning Way. - Sarah Perrin; Mathieu Laurière; Julien Pérolat; Matthieu Geist; Romuald Élie; Olivier Pietquin (IJCAI, 2021)

Twice regularized MDPs and the equivalence between robustness and regularization. - Esther Derman; Matthieu Geist; Shie Mannor (NeurIPS, 2021)

There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning. - Nathan Grinsztajn; Johan Ferret; Olivier Pietquin; Philippe Preux; Matthieu Geist (NeurIPS, 2021)

What Matters for Adversarial Imitation Learning? - Manu Orsini; Anton Raichuk; Léonard Hussenot; Damien Vincent; Robert Dadashi; Sertan Girgin; Matthieu Geist; Olivier Bachem; Olivier Pietquin; Marcin Andrychowicz (NeurIPS, 2021)

Scaling up Mean Field Games with Online Mirror Descent. - Julien Pérolat; Sarah Perrin; Romuald Elie; Mathieu Laurière; Georgios Piliouras; Matthieu Geist; Karl Tuyls; Olivier Pietquin (CoRR, 2021)

A functional mirror ascent view of policy gradient methods with function approximation. - Sharan Vaswani; Olivier Bachem; Simone Totaro; Robert Mueller; Matthieu Geist; Marlos C. Machado; Pablo Samuel Castro; Nicolas Le Roux (CoRR, 2021)

Evaluation of Prioritized Deep System Identification on a Path Following Task. - Antoine Mahé; Antoine Richard; Stéphanie Aravecchia; Matthieu Geist; Cédric Pradalier (J. Intell. Robotic Syst., 2021)

A FastSLAM Approach Integrating Beamforming Maps for Ultrasound-Based Robotic Inspection of Metal Structures. - Othmane-Latif Ouabi; Pascal Pomarede; Matthieu Geist; Nico F. Declercq; Cédric Pradalier (IEEE Robotics Autom. Lett., 2021)

How to Train Your HERON. - Antoine Richard; Stéphanie Aravecchia; Thomas Schillaci; Matthieu Geist; Cédric Pradalier (IEEE Robotics Autom. Lett., 2021)

Generalization in Mean Field Games by Learning Master Policies. - Sarah Perrin; Mathieu Laurière; Julien Pérolat; Romuald Élie; Matthieu Geist; Olivier Pietquin (AAAI, 2022)

Offline Reinforcement Learning as Anti-exploration. - Shideh Rezaeifar; Robert Dadashi; Nino Vieillard; Léonard Hussenot; Olivier Bachem; Olivier Pietquin; Matthieu Geist (AAAI, 2022)

A general class of surrogate functions for stable and efficient reinforcement learning. - Sharan Vaswani; Olivier Bachem; Simone Totaro; Robert Müller; Shivam Garg; Matthieu Geist; Marlos C. Machado; Pablo Samuel Castro; Nicolas Le Roux (AISTATS, 2022)

Implicitly Regularized RL with Implicit Q-values. - Nino Vieillard; Marcin Andrychowicz; Anton Raichuk; Olivier Pietquin; Matthieu Geist (AISTATS, 2022)

Concave Utility Reinforcement Learning: The Mean-field Game Viewpoint. - Matthieu Geist; Julien Pérolat; Mathieu Laurière; Romuald Elie; Sarah Perrin; Olivier Bachem; Rémi Munos; Olivier Pietquin (AAMAS, 2022)

Lazy-MDPs: Towards Interpretable RL by Learning When to Act. - Alexis Jacq; Johan Ferret; Olivier Pietquin; Matthieu Geist (AAMAS, 2022)

Scaling Mean Field Games by Online Mirror Descent. - Julien Pérolat; Sarah Perrin; Romuald Elie; Mathieu Laurière; Georgios Piliouras; Matthieu Geist; Karl Tuyls; Olivier Pietquin (AAMAS, 2022)

Polygonal Shapes Reconstruction from Acoustic Echoes Using a Mobile Sensor and Beamforming. - Othmane-Latif Ouabi; Jiawei Yi; Neil Zeghidour; Nico F. Declercq; Matthieu Geist; Cédric Pradalier (EUSIPCO, 2022)

Continuous Control with Action Quantization from Demonstrations. - Robert Dadashi; Léonard Hussenot; Damien Vincent; Sertan Girgin; Anton Raichuk; Matthieu Geist; Olivier Pietquin (ICML, 2022)

Large Batch Experience Replay. - Thibault Lahire; Matthieu Geist; Emmanuel Rachelson (ICML, 2022)

Scalable Deep Reinforcement Learning Algorithms for Mean Field Games. - Mathieu Laurière; Sarah Perrin; Sertan Girgin; Paul Muller; Ayush Jain; Theophile Cabannes; Georgios Piliouras; Julien Pérolat; Romuald Elie; Olivier Pietquin; Matthieu Geist (ICML, 2022)

Combined Grid and Feature-based Mapping of Metal Structures with Ultrasonic Guided Waves. - Othmane-Latif Ouabi; Ayoub Ridani; Pascal Pomarede; Neil Zeghidour; Nico F. Declercq; Matthieu Geist; Cédric Pradalier (ICRA, 2022)

Learning Energy Networks with Generalized Fenchel-Young Losses. - Mathieu Blondel; Felipe Llinares-López; Robert Dadashi; Léonard Hussenot; Matthieu Geist (NeurIPS, 2022)

Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act. - Alexis Jacq; Johan Ferret; Olivier Pietquin; Matthieu Geist (CoRR, 2022)

Learning Mean Field Games: A Survey. - Mathieu Laurière; Sarah Perrin; Matthieu Geist; Olivier Pietquin (CoRR, 2022)

KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. - Tadashi Kozuno; Wenhao Yang; Nino Vieillard; Toshinori Kitamura; Yunhao Tang; Jincheng Mei; Pierre Ménard; Mohammad Gheshlaghi Azar; Michal Valko; Rémi Munos; Olivier Pietquin; Matthieu Geist; et al. (CoRR, 2022)

Learning Correlated Equilibria in Mean-Field Games. - Paul Muller; Romuald Elie; Mark Rowland 001; Mathieu Laurière; Julien Pérolat; Sarah Perrin; Matthieu Geist; Georgios Piliouras; Olivier Pietquin; Karl Tuyls (CoRR, 2022)

C3PO: Learning to Achieve Arbitrary Goals via Massively Entropic Pretraining. - Alexis Jacq; Manu Orsini; Gabriel Dulac-Arnold; Olivier Pietquin; Matthieu Geist; Olivier Bachem (CoRR, 2022)

Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. - Paul Roit; Johan Ferret; Lior Shani; Roee Aharoni; Geoffrey Cideron; Robert Dadashi; Matthieu Geist; Sertan Girgin; Léonard Hussenot; Orgad Keller; Nikola Momchev; Sabela Ramos Garea; et al. (ACL (1), 2023)

Extreme Q-Learning: MaxEnt RL without Entropy. - Divyansh Garg; Joey Hejna; Matthieu Geist; Stefano Ermon (ICLR, 2023)

A Connection between One-Step RL and Critic Regularization in Reinforcement Learning. - Benjamin Eysenbach; Matthieu Geist; Sergey Levine; Ruslan Salakhutdinov (ICML, 2023)

Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. - Toshinori Kitamura; Tadashi Kozuno; Yunhao Tang; Nino Vieillard; Michal Valko; Wenhao Yang; Jincheng Mei; Pierre Ménard; Mohammad Gheshlaghi Azar; Rémi Munos; Olivier Pietquin; Matthieu Geist; et al. (ICML, 2023)

Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games. - Batuhan Yardim; Semih Cayci; Matthieu Geist; Niao He (ICML, 2023)

Pose-graph SLAM Using Multi-order Ultrasonic Echoes and Beamforming for Long-range Inspection Robots. - Othmane-Latif Ouabi; Neil Zeghidour; Nico F. Declercq; Matthieu Geist; Cédric Pradalier (ICRA, 2023)

Policy Gradient for Rectangular Robust Markov Decision Processes. - Navdeep Kumar; Esther Derman; Matthieu Geist; Kfir Y. Levy; Shie Mannor (NeurIPS, 2023)

On Imitation in Mean-field Games. - Giorgia Ramponi; Pavel Kolev; Olivier Pietquin; Niao He; Mathieu Laurière; Matthieu Geist (NeurIPS, 2023)

The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model. - Laixi Shi; Gen Li 005; Yuting Wei 001; Yuxin Chen 002; Matthieu Geist; Yuejie Chi (NeurIPS, 2023)

Offline Reinforcement Learning with On-Policy Q-Function Regularization. - Laixi Shi; Robert Dadashi; Yuejie Chi; Pablo Samuel Castro; Matthieu Geist (ECML/PKDD (4), 2023)

Policy Gradient for s-Rectangular Robust Markov Decision Processes. - Navdeep Kumar; Esther Derman; Matthieu Geist; Kfir Levy; Shie Mannor (CoRR, 2023)

Twice Regularized Markov Decision Processes: The Equivalence between Robustness and Regularization. - Esther Derman; Yevgeniy Men; Matthieu Geist; Shie Mannor (CoRR, 2023)

Get Back Here: Robust Imitation by Return-to-Distribution Planning. - Geoffrey Cideron; Baruch Tabanpour; Sebastian Curi; Sertan Girgin; Léonard Hussenot; Gabriel Dulac-Arnold; Matthieu Geist; Olivier Pietquin; Robert Dadashi (CoRR, 2023)

GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models. - Rishabh Agarwal; Nino Vieillard; Piotr Stanczyk; Sabela Ramos; Matthieu Geist; Olivier Bachem (CoRR, 2023)

A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning. - Benjamin Eysenbach; Matthieu Geist; Sergey Levine; Ruslan Salakhutdinov (CoRR, 2023)

DRIFT: Deep Reinforcement Learning for Intelligent Floating Platforms Trajectories. - Matteo El Hariry; Antoine Richard; Vivek Muralidharan; Baris Can Yalçin; Matthieu Geist; Miguel A. Olivares-Méndez (CoRR, 2023)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Zhaohan Daniel Guo; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Andrea Michi; Marco Selvi; Sertan Girgin; et al. (CoRR, 2023)

Learning Discrete-Time Major-Minor Mean Field Games. - Kai Cui 001; Gökçe Dayanikli; Mathieu Laurière; Matthieu Geist; Olivier Pietquin; Heinz Koeppl (AAAI, 2024)

Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning. - Zida Wu; Mathieu Laurière; Samuel Jia Cong Chua; Matthieu Geist; Olivier Pietquin; Ankur Mehta (AAMAS, 2024)

Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion. - Yannis Flet-Berliac; Nathan Grinsztajn; Florian Strub; Eugene Choi; Bill Wu; Chris Cremer; Arash Ahmadian; Yash Chandak; Mohammad Gheshlaghi Azar; Olivier Pietquin; Matthieu Geist (EMNLP, 2024)

On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes. - Rishabh Agarwal; Nino Vieillard; Yongchao Zhou; Piotr Stanczyk; Sabela Ramos Garea; Matthieu Geist; Olivier Bachem (ICLR, 2024)

Closing the Gap between TD Learning and Supervised Learning - A Generalisation Point of View. - Raj Ghugare; Matthieu Geist; Glen Berseth; Benjamin Eysenbach (ICLR, 2024)

MusicRL: Aligning Music Generation to Human Preferences. - Geoffrey Cideron; Sertan Girgin; Mauro Verzetti; Damien Vincent; Matej Kastelic; Zalán Borsos; Brian McWilliams; Victor Ungureanu; Olivier Bachem; Olivier Pietquin; Matthieu Geist; Léonard Hussenot; et al. (ICML, 2024)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Daniel Guo 001; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Côme Fiegel; Andrea Michi; Marco Selvi; et al. (ICML, 2024)

DRIFT: Deep Reinforcement Learning for Intelligent Floating Platforms Trajectories. - Matteo El Hariry; Antoine Richard; Vivek Muralidharan; Matthieu Geist; Miguel A. Olivares-Méndez (IROS, 2024)

Near-Optimal Distributionally Robust Reinforcement Learning with General $L_p$ Norms. - Pierre Clavier; Laixi Shi; Erwan Le Pennec; Eric Mazumdar; Adam Wierman; Matthieu Geist (NeurIPS, 2024)

Periodic agent-state based Q-learning for POMDPs. - Amit Sinha; Matthieu Geist; Aditya Mahajan (NeurIPS, 2024)

Imitating Language via Scalable Inverse Reinforcement Learning. - Markus Wulfmeier; Michael Bloesch; Nino Vieillard; Arun Ahuja; Jörg Bornschein; Sandy H. Huang; Artem Sokolov; Matt Barnes 001; Guillaume Desjardins; Alex Bewley; Sarah Bechtle; Jost Tobias Springenberg; et al. (NeurIPS, 2024)

Time-Constrained Robust MDPs. - Adil Zouitine; David Bertoin; Pierre Clavier; Matthieu Geist; Emmanuel Rachelson (NeurIPS, 2024)

Towards Minimax Optimality of Model-based Robust Reinforcement Learning. - Pierre Clavier; Erwan Le Pennec; Matthieu Geist (UAI, 2024)

Leveraging Procedural Generation for Learning Autonomous Peg-in-Hole Assembly in Space. - Andrej Orsula; Matthieu Geist; Miguel A. Olivares-Méndez; Carol Chamorro-Martínez (CoRR, 2024)

Bootstrapping Expectiles in Reinforcement Learning. - Pierre Clavier; Emmanuel Rachelson; Erwan Le Pennec; Matthieu Geist (CoRR, 2024)

RRLS : Robust Reinforcement Learning Suite. - Adil Zouitine; David Bertoin; Pierre Clavier; Matthieu Geist; Emmanuel Rachelson (CoRR, 2024)

Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion. - Yannis Flet-Berliac; Nathan Grinsztajn; Florian Strub; Eugene Choi; Chris Cremer; Arash Ahmadian; Yash Chandak; Mohammad Gheshlaghi Azar; Olivier Pietquin; Matthieu Geist (CoRR, 2024)

Averaging log-likelihoods in direct alignment. - Nathan Grinsztajn; Yannis Flet-Berliac; Mohammad Gheshlaghi Azar; Florian Strub; Bill Wu; Eugene Choi; Chris Cremer; Arash Ahmadian; Yash Chandak; Olivier Pietquin; Matthieu Geist (CoRR, 2024)

Solving robust MDPs as a sequence of static RL problems. - Adil Zouitine; Matthieu Geist; Emmanuel Rachelson (CoRR, 2024)

Understanding Likelihood Over-optimisation in Direct Alignment Algorithms. - Zhengyan Shi; Sander Land; Acyr Locatelli; Matthieu Geist; Max Bartolo (CoRR, 2024)

A Survey of Temporal Credit Assignment in Deep Reinforcement Learning. - Eduardo Pignatelli; Johan Ferret; Matthieu Geist; Thomas Mesnard; Hado van Hasselt; Laura Toni (Trans. Mach. Learn. Res., 2024)

Self-Improving Robust Preference Optimization. - Eugene Choi; Arash Ahmadian; Matthieu Geist; Olivier Pietquin; Mohammad Gheshlaghi Azar (ICLR, 2025)

Command A: An Enterprise-Ready Large Language Model. - Aakanksha; Arash Ahmadian; Marwan Ahmed; Jay Alammar; Milad Alizadeh; Yazeed Alnumay; Sophia Althammer; Arkady Arkhangorodsky; Viraat Aryabumi; Dennis Aumiller; Raphaël Avalos; Zahara Aviv; et al. (CoRR, 2025)

ShiQ: Bringing back Bellman to LLMs. - Pierre Clavier; Nathan Grinsztajn; Raphaël Avalos; Yannis Flet-Berliac; Irem Ergün; Omar Darwiche Domingues; Eugene Tarassov; Olivier Pietquin; Pierre H. Richemond; Florian Strub; Matthieu Geist (CoRR, 2025)

NavBench: A Unified Robotics Benchmark for Reinforcement Learning-Based Autonomous Navigation. - Matteo El Hariry; Antoine Richard; Ricard M. Castan; Luis F. W. Batista; Matthieu Geist; Cédric Pradalier; Miguel A. Olivares-Méndez (CoRR, 2025)

Learning Equilibria from Data: Provably Efficient Multi-Agent Imitation Learning. - Till Freihaut; Luca Viano; Volkan Cevher; Matthieu Geist; Giorgia Ramponi (CoRR, 2025)

Sim2Dust: Mastering Dynamic Waypoint Tracking on Granular Media. - Andrej Orsula; Matthieu Geist; Miguel A. Olivares-Méndez; Carol Martínez (CoRR, 2025)

What Matters for Bioacoustic Encoding. - Marius Miron; David Robinson; Milad Alizadeh; Ellen Gilsenan-McMahon; Gagan Narula; Emmanuel Chemla; Maddie Cusimano; Felix Effenberger; Masato Hagiwara; Benjamin Hoffman; Sara Keen; Diane Kim; et al. (CoRR, 2025)