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Journal Articles
- 2024
[j15]Haoyang Cao
, Xin Guo, Mathieu Laurière:
Connecting GANs, Mean-Field Games, and Optimal Transport. SIAM J. Appl. Math. 84(4): 1255-1287 (2024)- 2022
- [j14]Alexander Aurell, René Carmona, Gökçe Dayanikli, Mathieu Laurière:
Finite State Graphon Games with Applications to Epidemics. Dyn. Games Appl. 12(1): 49-81 (2022) - [j13]René Carmona, Gökçe Dayanikli, Mathieu Laurière:
Mean Field Models to Regulate Carbon Emissions in Electricity Production. Dyn. Games Appl. 12(3): 897-928 (2022) - [j12]Maximilien Germain, Mathieu Laurière, Huyên Pham, Xavier Warin:
DeepSets and Their Derivative Networks for Solving Symmetric PDEs. J. Sci. Comput. 91(2): 63 (2022) - [j11]Andrea Angiuli, Jean-Pierre Fouque, Mathieu Laurière:
Unified reinforcement Q-learning for mean field game and control problems. Math. Control. Signals Syst. 34(2): 217-271 (2022) - [j10]René Carmona, Daniel B. Cooney, Christy V. Graves, Mathieu Laurière:
Stochastic Graphon Games: I. The Static Case. Math. Oper. Res. 47(1): 750-778 (2022) - [j9]Alexander Aurell, René Carmona, Gökçe Dayanikli, Mathieu Laurière:
Optimal Incentives to Mitigate Epidemics: A Stackelberg Mean Field Game Approach. SIAM J. Control. Optim. 60(2): S294-S322 (2022) - [j8]Mathieu Laurière, Ludovic Tangpi:
Convergence of Large Population Games to Mean Field Games with Interaction Through the Controls. SIAM J. Math. Anal. 54(3): 3535-3574 (2022) - 2021
- [j7]René Carmona, Mathieu Laurière:
Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games I: The Ergodic Case. SIAM J. Numer. Anal. 59(3): 1455-1485 (2021) - 2020
- [j6]Titouan Carette, Mathieu Laurière, Frédéric Magniez:
Extended Learning Graphs for Triangle Finding. Algorithmica 82(4): 980-1005 (2020) - 2019
- [j5]Mathieu Laurière, Laurent Mertz:
Penalization of Nonsmooth Dynamical Systems with Noise: Ergodicity and Asymptotic Formulae for Threshold Crossings Probabilities. SIAM J. Appl. Dyn. Syst. 18(2): 853-880 (2019) - 2018
- [j4]Cyril Feau, Mathieu Laurière, Laurent Mertz:
Asymptotic formulae for the risk of failure related to an elasto-plastic problem with noise. Asymptot. Anal. 106(1): 47-60 (2018) - 2016
- [j3]Mathieu Laurière, Olivier Pironneau:
Dynamic Programming for Mean-Field Type Control. J. Optim. Theory Appl. 169(3): 902-924 (2016) - [j2]Iordanis Kerenidis, Mathieu Laurière, Francois Le Gall, Mathys Rennela:
Information cost of quantum communication protocols. Quantum Inf. Comput. 16(3&4): 181-196 (2016) - [j1]Lila Fontes, Rahul Jain, Iordanis Kerenidis, Sophie Laplante, Mathieu Laurière, Jérémie Roland:
Relative Discrepancy Does Not Separate Information and Communication Complexity. ACM Trans. Comput. Theory 9(1): 4:1-4:15 (2016)
Conference and Workshop Papers
- 2024
- [c25]Kai Cui, Gökçe Dayanikli, Mathieu Laurière, Matthieu Geist, Olivier Pietquin, Heinz Koeppl:
Learning Discrete-Time Major-Minor Mean Field Games. AAAI 2024: 9616-9625 - [c24]Gökçe Dayanikli, Mathieu Laurière:
Multi-population Mean Field Games with Multiple Major Players: Application to Carbon Emission Regulations. ACC 2024: 5075-5081 - [c23]Gökçe Dayanikli, Mathieu Laurière, Jiacheng Zhang:
Deep Learning for Population-Dependent Controls in Mean Field Control Problems with Common Noise. AAMAS 2024: 2231-2233 - [c22]Zida Wu, Mathieu Laurière, Samuel Jia Cong Chua, Matthieu Geist, Olivier Pietquin, Ankur Mehta:
Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning. AAMAS 2024: 2561-2563 - [c21]Muhammad Aneeq uz Zaman, Mathieu Laurière, Alec Koppel, Tamer Basar:
Robust cooperative multi-agent reinforcement learning: A mean-field type game perspective. L4DC 2024: 770-783 - 2023
- [c20]Diogo Gomes, Julian Gutierrez, Mathieu Laurière:
Machine Learning Architectures for Price Formation Models with Common Noise. CDC 2023: 4345-4350 - [c19]Giorgia Ramponi, Pavel Kolev, Olivier Pietquin, Niao He, Mathieu Laurière, Matthieu Geist:
On Imitation in Mean-field Games. NeurIPS 2023 - [c18]Lila Fontes, Sophie Laplante, Mathieu Laurière, Alexandre Nolin:
The Communication Complexity of Functions with Large Outputs. SIROCCO 2023: 427-458 - 2022
- [c17]Sarah Perrin, Mathieu Laurière, Julien Pérolat, Romuald Élie, Matthieu Geist, Olivier Pietquin:
Generalization in Mean Field Games by Learning Master Policies. AAAI 2022: 9413-9421 - [c16]Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin:
Concave Utility Reinforcement Learning: The Mean-field Game Viewpoint. AAMAS 2022: 489-497 - [c15]Paul Muller, Mark Rowland, Romuald Elie, Georgios Piliouras, Julien Pérolat, Mathieu Laurière, Raphaël Marinier, Olivier Pietquin, Karl Tuyls:
Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO. AAMAS 2022: 926-934 - [c14]Julien Pérolat, Sarah Perrin, Romuald Elie, Mathieu Laurière, Georgios Piliouras, Matthieu Geist, Karl Tuyls, Olivier Pietquin:
Scaling Mean Field Games by Online Mirror Descent. AAMAS 2022: 1028-1037 - [c13]Theophile Cabannes, Mathieu Laurière, Julien Pérolat, Raphaël Marinier, Sertan Girgin, Sarah Perrin, Olivier Pietquin, Alexandre M. Bayen, Eric Goubault, Romuald Elie:
Solving N-Player Dynamic Routing Games with Congestion: A Mean-Field Approach. AAMAS 2022: 1557-1559 - [c12]Jimin Lin, Andrea Angiuli, Nils Detering, Jean-Pierre Fouque, Mathieu Laurière:
Reinforcement Learning for Intra-and-Inter-Bank Borrowing and Lending Mean Field Control Game. ICAIF 2022: 369-376 - [c11]Mathieu Laurière, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain, Theophile Cabannes, Georgios Piliouras, Julien Pérolat, Romuald Elie, Olivier Pietquin, Matthieu Geist:
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games. ICML 2022: 12078-12095 - 2021
- [c10]Sarah Perrin, Mathieu Laurière, Julien Pérolat, Matthieu Geist, Romuald Élie, Olivier Pietquin:
Mean Field Games Flock! The Reinforcement Learning Way. IJCAI 2021: 356-362 - 2020
- [c9]Romuald Elie, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Olivier Pietquin:
On the Convergence of Model Free Learning in Mean Field Games. AAAI 2020: 7143-7150 - [c8]René Carmona, Kenza Hamidouche, Mathieu Laurière, Zongjun Tan:
Policy Optimization for Linear-Quadratic Zero-Sum Mean-Field Type Games. CDC 2020: 1038-1043 - [c7]Sarah Perrin, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Romuald Elie, Olivier Pietquin:
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications. NeurIPS 2020 - 2017
- [c6]Mathieu Laurière, Dave Touchette:
The Flow of Information in Interactive Quantum Protocols: the Cost of Forgetting. ITCS 2017: 47:1-47:1 - [c5]Titouan Carette, Mathieu Laurière, Frédéric Magniez:
Extended Learning Graphs for Triangle Finding. STACS 2017: 20:1-20:14 - 2016
- [c4]Charles Bascou, Valentin Emiya, Mathieu Laurière:
The problem of musical gesture continuation and a baseline system. ICMC 2016 - [c3]Sophie Laplante, Mathieu Laurière, Alexandre Nolin, Jérémie Roland, Gabriel Senno:
Robust Bell Inequalities from Communication Complexity. TQC 2016: 5:1-5:24 - 2015
- [c2]Lila Fontes, Rahul Jain, Iordanis Kerenidis, Sophie Laplante, Mathieu Laurière, Jérémie Roland:
Relative Discrepancy Does not Separate Information and Communication Complexity. ICALP (1) 2015: 506-516 - 2013
- [c1]Iordanis Kerenidis, Mathieu Laurière, David Xiao:
New Lower Bounds for Privacy in Communication Protocols. ICITS 2013: 69-89
Informal and Other Publications
- 2024
- [i48]Mathieu Laurière, Ludovic Tangpi, Xuchen Zhou:
A Deep Learning Method for Optimal Investment Under Relative Performance Criteria Among Heterogeneous Agents. CoRR abs/2402.07365 (2024) - [i47]Zida Wu, Mathieu Laurière, Samuel Jia Cong Chua, Matthieu Geist, Olivier Pietquin, Ankur Mehta:
Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning. CoRR abs/2403.03552 (2024) - [i46]Muhammad Aneeq uz Zaman, Alec Koppel, Mathieu Laurière, Tamer Basar:
Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective. CoRR abs/2403.11345 (2024) - [i45]Andrea Angiuli, Jean-Pierre Fouque, Mathieu Laurière, Mengrui Zhang:
Analysis of Multiscale Reinforcement Q-Learning Algorithms for Mean Field Control Games. CoRR abs/2405.17017 (2024) - [i44]Zhouzhou Gu, Mathieu Laurière, Sebastian Merkel, Jonathan Payne:
Global Solutions to Master Equations for Continuous Time Heterogeneous Agent Macroeconomic Models. CoRR abs/2406.13726 (2024) - [i43]Muhammad Aneeq uz Zaman, Mathieu Laurière, Alec Koppel, Tamer Basar:
Robust Cooperative Multi-Agent Reinforcement Learning:A Mean-Field Type Game Perspective. CoRR abs/2406.13992 (2024) - [i42]Kai Shao, Jiacheng Shen, Chijie An, Mathieu Laurière:
Reinforcement Learning for Finite Space Mean-Field Type Games. CoRR abs/2409.18152 (2024) - 2023
- [i41]Sebastian Baudelet, Brieuc Frénais, Mathieu Laurière, Amal Machtalay, Yuchen Zhu:
Deep Learning for Mean Field Optimal Transport. CoRR abs/2302.14739 (2023) - [i40]Ruimeng Hu, Mathieu Laurière:
Recent Developments in Machine Learning Methods for Stochastic Control and Games. CoRR abs/2303.10257 (2023) - [i39]Lila Fontes, Sophie Laplante, Mathieu Laurière, Alexandre Nolin:
The communication complexity of functions with large outputs. CoRR abs/2304.00391 (2023) - [i38]Giorgia Ramponi, Pavel Kolev, Olivier Pietquin, Niao He, Mathieu Laurière, Matthieu Geist:
On Imitation in Mean-field Games. CoRR abs/2306.14799 (2023) - [i37]René A. Carmona, Gökçe Dayanikli, François Delarue, Mathieu Laurière:
From Nash Equilibrium to Social Optimum and vice versa: a Mean Field Perspective. CoRR abs/2312.10526 (2023) - [i36]Kai Cui, Gökçe Dayanikli, Mathieu Laurière, Matthieu Geist, Olivier Pietquin, Heinz Koeppl:
Learning Discrete-Time Major-Minor Mean Field Games. CoRR abs/2312.10787 (2023) - [i35]Lila Fontes, Sophie Laplante, Mathieu Laurière, Alexandre Nolin:
The communication complexity of functions with large outputs. Electron. Colloquium Comput. Complex. TR23 (2023) - 2022
- [i34]Mathieu Laurière, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain, Theophile Cabannes, Georgios Piliouras, Julien Pérolat, Romuald Élie, Olivier Pietquin, Matthieu Geist:
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games. CoRR abs/2203.11973 (2022) - [i33]Mathieu Laurière, Sarah Perrin, Matthieu Geist, Olivier Pietquin:
Learning Mean Field Games: A Survey. CoRR abs/2205.12944 (2022) - [i32]Paul Muller, Romuald Elie, Mark Rowland, Mathieu Laurière, Julien Pérolat, Sarah Perrin, Matthieu Geist, Georgios Piliouras, Olivier Pietquin, Karl Tuyls:
Learning Correlated Equilibria in Mean-Field Games. CoRR abs/2208.10138 (2022) - 2021
- [i31]Julien Pérolat, Sarah Perrin, Romuald Elie, Mathieu Laurière, Georgios Piliouras, Matthieu Geist, Karl Tuyls, Olivier Pietquin:
Scaling up Mean Field Games with Online Mirror Descent. CoRR abs/2103.00623 (2021) - [i30]Sarah Perrin, Mathieu Laurière, Julien Pérolat, Matthieu Geist, Romuald Élie, Olivier Pietquin:
Mean Field Games Flock! The Reinforcement Learning Way. CoRR abs/2105.07933 (2021) - [i29]Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin:
Concave Utility Reinforcement Learning: the Mean-field Game viewpoint. CoRR abs/2106.03787 (2021) - [i28]Mathieu Laurière:
Numerical Methods for Mean Field Games and Mean Field Type Control. CoRR abs/2106.06231 (2021) - [i27]Andrea Angiuli, Jean-Pierre Fouque, Mathieu Laurière:
Reinforcement Learning for Mean Field Games, with Applications to Economics. CoRR abs/2106.13755 (2021) - [i26]René Carmona, Mathieu Laurière:
Deep Learning for Mean Field Games and Mean Field Control with Applications to Finance. CoRR abs/2107.04568 (2021) - [i25]Mathieu Laurière, Gilles Pagès, Olivier Pironneau:
Performance of a Markovian neural network versus dynamic programming on a fishing control problem. CoRR abs/2109.06856 (2021) - [i24]Sarah Perrin, Mathieu Laurière, Julien Pérolat, Romuald Élie, Matthieu Geist, Olivier Pietquin:
Generalization in Mean Field Games by Learning Master Policies. CoRR abs/2109.09717 (2021) - [i23]Mathieu Laurière, Jiahao Song, Qing Tang:
Policy iteration method for time-dependent Mean Field Games systems with non-separable Hamiltonians. CoRR abs/2110.02552 (2021) - [i22]Theophile Cabannes, Mathieu Laurière, Julien Pérolat, Raphaël Marinier, Sertan Girgin, Sarah Perrin, Olivier Pietquin, Alexandre M. Bayen, Éric Goubault, Romuald Elie:
Solving N-player dynamic routing games with congestion: a mean field approach. CoRR abs/2110.11943 (2021) - [i21]Paul Muller, Mark Rowland, Romuald Elie, Georgios Piliouras, Julien Pérolat, Mathieu Laurière, Raphaël Marinier, Olivier Pietquin, Karl Tuyls:
Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO. CoRR abs/2111.08350 (2021) - 2020
- [i20]Haoyang Cao, Xin Guo, Mathieu Laurière:
Connecting GANs and MFGs. CoRR abs/2002.04112 (2020) - [i19]Yves Achdou, Mathieu Laurière:
Mean Field Games and Applications: Numerical Aspects. CoRR abs/2003.04444 (2020) - [i18]Arthur Charpentier, Romuald Elie, Mathieu Laurière, Viet Chi Tran:
COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability. CoRR abs/2005.06526 (2020) - [i17]Laura Leal, Mathieu Laurière, Charles-Albert Lehalle:
Learning a functional control for high-frequency finance. CoRR abs/2006.09611 (2020) - [i16]Andrea Angiuli, Jean-Pierre Fouque, Mathieu Laurière:
Unified Reinforcement Q-Learning for Mean Field Game and Control Problems. CoRR abs/2006.13912 (2020) - [i15]Sarah Perrin, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Romuald Elie, Olivier Pietquin:
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications. CoRR abs/2007.03458 (2020) - [i14]René Carmona, Kenza Hamidouche, Mathieu Laurière, Zongjun Tan:
Linear-Quadratic Zero-Sum Mean-Field Type Games: Optimality Conditions and Policy Optimization. CoRR abs/2009.00578 (2020) - [i13]René Carmona, Kenza Hamidouche, Mathieu Laurière, Zongjun Tan:
Policy Optimization for Linear-Quadratic Zero-Sum Mean-Field Type Games. CoRR abs/2009.02146 (2020) - 2019
- [i12]Romuald Elie, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Olivier Pietquin:
Approximate Fictitious Play for Mean Field Games. CoRR abs/1907.02633 (2019) - [i11]René Carmona, Mathieu Laurière:
Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games: I - The Ergodic Case. CoRR abs/1907.05980 (2019) - [i10]René Carmona, Mathieu Laurière:
Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games: II - The Finite Horizon Case. CoRR abs/1908.01613 (2019) - [i9]René Carmona, Mathieu Laurière, Zongjun Tan:
Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods. CoRR abs/1910.04295 (2019) - [i8]René Carmona, Mathieu Laurière, Zongjun Tan:
Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning. CoRR abs/1910.12802 (2019) - 2017
- [i7]Mathieu Laurière, Dave Touchette:
The Flow of Information in Interactive Quantum Protocols: the Cost of Forgetting. CoRR abs/1701.02062 (2017) - 2016
- [i6]Sophie Laplante, Mathieu Laurière, Alexandre Nolin, Jérémie Roland, Gabriel Senno:
Robust Bell inequalities from communication complexity. CoRR abs/1606.09514 (2016) - [i5]Titouan Carette, Mathieu Laurière, Frédéric Magniez:
Extended Learning Graphs for Triangle Finding. CoRR abs/1609.07786 (2016) - [i4]Yves Achdou, Mathieu Laurière:
Mean Field Type Control with Congestion (II): An Augmented Lagrangian Method. CoRR abs/1611.02023 (2016) - 2015
- [i3]Lila Fontes, Rahul Jain, Iordanis Kerenidis, Sophie Laplante, Mathieu Laurière, Jérémie Roland:
Relative Discrepancy does not separate Information and Communication Complexity. Electron. Colloquium Comput. Complex. TR15 (2015) - 2014
- [i2]Iordanis Kerenidis, Mathieu Laurière, François Le Gall, Mathys Rennela:
Privacy in Quantum Communication Complexity. CoRR abs/1409.8488 (2014) - 2013
- [i1]Iordanis Kerenidis, Mathieu Laurière, David Xiao:
New lower bounds for privacy in communication protocols. Electron. Colloquium Comput. Complex. TR13 (2013)
Coauthor Index
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