New lower bounds for privacy in communication protocols. - Iordanis Kerenidis; Mathieu Laurière; David Xiao (Electron. Colloquium Comput. Complex., 2013)

Privacy in Quantum Communication Complexity. - Iordanis Kerenidis; Mathieu Laurière; François Le Gall; Mathys Rennela (CoRR, 2014)

Relative Discrepancy does not separate Information and Communication Complexity. - Lila Fontes; Rahul Jain 001; Iordanis Kerenidis; Sophie Laplante; Mathieu Laurière; Jérémie Roland (Electron. Colloquium Comput. Complex., 2015)

The problem of musical gesture continuation and a baseline system. - Charles Bascou; Valentin Emiya; Mathieu Laurière (ICMC, 2016)

Mean Field Type Control with Congestion (II): An Augmented Lagrangian Method. - Yves Achdou; Mathieu Laurière (CoRR, 2016)

Dynamic Programming for Mean-Field Type Control. - Mathieu Laurière; Olivier Pironneau (J. Optim. Theory Appl., 2016)

Information cost of quantum communication protocols. - Iordanis Kerenidis; Mathieu Laurière; Francois Le Gall; Mathys Rennela (Quantum Inf. Comput., 2016)

The Flow of Information in Interactive Quantum Protocols: the Cost of Forgetting. - Mathieu Laurière; Dave Touchette (ITCS, 2017)

Asymptotic formulae for the risk of failure related to an elasto-plastic problem with noise. - Cyril Feau; Mathieu Laurière; Laurent Mertz (Asymptot. Anal., 2018)

Robust Bell inequalities from communication complexity. - Sophie Laplante; Mathieu Laurière; Alexandre Nolin; Jérémie Roland; Gabriel Senno (Quantum, 2018)

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

Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games: II - The Finite Horizon Case. - René Carmona 001; Mathieu Laurière (CoRR, 2019)

Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods. - René Carmona 001; Mathieu Laurière; Zongjun Tan (CoRR, 2019)

Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning. - René Carmona 001; Mathieu Laurière; Zongjun Tan (CoRR, 2019)

Penalization of Nonsmooth Dynamical Systems with Noise: Ergodicity and Asymptotic Formulae for Threshold Crossings Probabilities. - Mathieu Laurière; Laurent Mertz (SIAM J. Appl. Dyn. 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)

Policy Optimization for Linear-Quadratic Zero-Sum Mean-Field Type Games. - René Carmona 001; Kenza Hamidouche; Mathieu Laurière; Zongjun Tan (CDC, 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)

Extended Learning Graphs for Triangle Finding. - Titouan Carette; Mathieu Laurière; Frédéric Magniez (Algorithmica, 2020)

Connecting GANs and MFGs. - Haoyang Cao; Xin Guo 001; Mathieu Laurière (CoRR, 2020)

Mean Field Games and Applications: Numerical Aspects. - Yves Achdou; Mathieu Laurière (CoRR, 2020)

COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability. - Arthur Charpentier; Romuald Elie; Mathieu Laurière; Viet Chi Tran (CoRR, 2020)

Learning a functional control for high-frequency finance. - Laura Leal; Mathieu Laurière; Charles-Albert Lehalle (CoRR, 2020)

Linear-Quadratic Zero-Sum Mean-Field Type Games: Optimality Conditions and Policy Optimization. - René Carmona 001; Kenza Hamidouche; Mathieu Laurière; Zongjun Tan (CoRR, 2020)

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

Numerical Methods for Mean Field Games and Mean Field Type Control. - Mathieu Laurière (CoRR, 2021)

Reinforcement Learning for Mean Field Games, with Applications to Economics. - Andrea Angiuli; Jean-Pierre Fouque; Mathieu Laurière (CoRR, 2021)

Deep Learning for Mean Field Games and Mean Field Control with Applications to Finance. - René Carmona 001; Mathieu Laurière (CoRR, 2021)

Performance of a Markovian neural network versus dynamic programming on a fishing control problem. - Mathieu Laurière; Gilles Pagès; Olivier Pironneau (CoRR, 2021)

Policy iteration method for time-dependent Mean Field Games systems with non-separable Hamiltonians. - Mathieu Laurière; Jiahao Song; Qing Tang (CoRR, 2021)

Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games I: The Ergodic Case. - René Carmona 001; Mathieu Laurière (SIAM J. Numer. Anal., 2021)

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

Solving N-Player Dynamic Routing Games with Congestion: A Mean-Field Approach. - Theophile Cabannes; Mathieu Laurière; Julien Pérolat; Raphaël Marinier; Sertan Girgin; Sarah Perrin; Olivier Pietquin; Alexandre M. Bayen; Eric Goubault; Romuald Elie (AAMAS, 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)

Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO. - Paul Muller; Mark Rowland 001; Romuald Elie; Georgios Piliouras; Julien Pérolat; Mathieu Laurière; Raphaël Marinier; Olivier Pietquin; Karl Tuyls (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)

Reinforcement Learning for Intra-and-Inter-Bank Borrowing and Lending Mean Field Control Game. - Jimin Lin; Andrea Angiuli; Nils Detering; Jean-Pierre Fouque; Mathieu Laurière (ICAIF, 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)

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

Finite State Graphon Games with Applications to Epidemics. - Alexander Aurell; René Carmona 001; Gökçe Dayanikli; Mathieu Laurière (Dyn. Games Appl., 2022)

Mean Field Models to Regulate Carbon Emissions in Electricity Production. - René Carmona 001; Gökçe Dayanikli; Mathieu Laurière (Dyn. Games Appl., 2022)

DeepSets and Their Derivative Networks for Solving Symmetric PDEs. - Maximilien Germain; Mathieu Laurière; Huyên Pham; Xavier Warin (J. Sci. Comput., 2022)

Unified reinforcement Q-learning for mean field game and control problems. - Andrea Angiuli; Jean-Pierre Fouque; Mathieu Laurière (Math. Control. Signals Syst., 2022)

Stochastic Graphon Games: I. The Static Case. - René Carmona 001; Daniel B. Cooney; Christy V. Graves; Mathieu Laurière (Math. Oper. Res., 2022)

Optimal Incentives to Mitigate Epidemics: A Stackelberg Mean Field Game Approach. - Alexander Aurell; René Carmona 001; Gökçe Dayanikli; Mathieu Laurière (SIAM J. Control. Optim., 2022)

Convergence of Large Population Games to Mean Field Games with Interaction Through the Controls. - Mathieu Laurière; Ludovic Tangpi (SIAM J. Math. Anal., 2022)

Machine Learning Architectures for Price Formation Models with Common Noise. - Diogo Gomes 002; Julian Gutierrez 003; Mathieu Laurière (CDC, 2023)

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

Deep Learning for Mean Field Optimal Transport. - Sebastian Baudelet; Brieuc Frénais; Mathieu Laurière; Amal Machtalay; Yuchen Zhu (CoRR, 2023)

Recent Developments in Machine Learning Methods for Stochastic Control and Games. - Ruimeng Hu; Mathieu Laurière (CoRR, 2023)

From Nash Equilibrium to Social Optimum and vice versa: a Mean Field Perspective. - René A. Carmona; Gökçe Dayanikli; François Delarue; Mathieu Laurière (CoRR, 2023)

The communication complexity of functions with large outputs. - Lila Fontes; Sophie Laplante; Mathieu Laurière; Alexandre Nolin (Electron. Colloquium Comput. Complex., 2023)

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

Multi-population Mean Field Games with Multiple Major Players: Application to Carbon Emission Regulations. - Gökçe Dayanikli; Mathieu Laurière (ACC, 2024)

Deep Learning for Population-Dependent Controls in Mean Field Control Problems with Common Noise. - Gökçe Dayanikli; Mathieu Laurière; Jiacheng Zhang (AAMAS, 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)

How can the tragedy of the commons be prevented?: Introducing Linear Quadratic Mixed Mean Field Games. - Gökçe Dayanikli; Mathieu Laurière (CDC, 2024)

Robust cooperative multi-agent reinforcement learning: A mean-field type game perspective. - Muhammad Aneeq uz Zaman; Mathieu Laurière; Alec Koppel; Tamer Basar (L4DC, 2024)

Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective. - Muhammad Aneeq uz Zaman; Alec Koppel; Mathieu Laurière; Tamer Basar (CoRR, 2024)

Analysis of Multiscale Reinforcement Q-Learning Algorithms for Mean Field Control Games. - Andrea Angiuli; Jean-Pierre Fouque; Mathieu Laurière; Mengrui Zhang (CoRR, 2024)

Global Solutions to Master Equations for Continuous Time Heterogeneous Agent Macroeconomic Models. - Zhouzhou Gu; Mathieu Laurière; Sebastian Merkel; Jonathan Payne (CoRR, 2024)

Robust Cooperative Multi-Agent Reinforcement Learning:A Mean-Field Type Game Perspective. - Muhammad Aneeq uz Zaman; Mathieu Laurière; Alec Koppel; Tamer Basar (CoRR, 2024)

Reinforcement Learning for Finite Space Mean-Field Type Games. - Kai Shao; Jiacheng Shen; Chijie An; Mathieu Laurière (CoRR, 2024)

A Simulation-Free Deep Learning Approach to Stochastic Optimal Control. - Mengjian Hua; Mathieu Laurière; Eric Vanden-Eijnden (CoRR, 2024)

Deep Learning Algorithms for Mean Field Optimal Stopping in Finite Space and Discrete Time. - Lorenzo Magnino; Yuchen Zhu; Mathieu Laurière (CoRR, 2024)

Connecting GANs, Mean-Field Games, and Optimal Transport. - Haoyang Cao; Xin Guo 001; Mathieu Laurière (SIAM J. Appl. Math., 2024)

A Game-Theoretic Framework for Generic Second-Order Traffic Flow Models Using Mean Field Games and Adversarial Inverse Reinforcement Learning. - Zhaobin Mo; Xu Chen; Xuan Di; Elisa Iacomini; Chiara Segala; Michael Herty; Mathieu Laurière (Transp. Sci., 2024)

A deep learning method for optimal investment under relative performance criteria among heterogeneous agents. - Mathieu Laurière; Ludovic Tangpi; Xuchen Zhou (Eur. J. Oper. Res., 2025)

Learning Equilibria in Cournot Mean Field Games of Controls. - Fabio Camilli; Mathieu Laurière; Qing Tang (SIAM J. Control. Optim., 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)