Research
IEOR researchers investigate the latest mathematical tools, approaches, and methodologies to make new theoretical discoveries and innovations that touch nearly every industry, making them more efficient and profitable in areas such as supply chain, logistics, manufacturing, data science, energy systems, robotics, and management.
Selected Publications
Mean-Field Controls with Q-Learning for Cooperative MARL: Convergence and Complexity Analysis
Interbank lending with benchmark rates: Pareto optima for a class of singular control games
, , . Interbank lending with benchmark rates: Pareto optima for a class of singular control games. Mathematical Finance. 2021; 31: 1357– 1393. https://doi.org/10.1111/mafi.12325
A Class of Stochastic Games and Moving Free Boundary Problems
X. Guo, W. Tang, R. Xu, “A class of stochastic games and moving free boundary problems”. SIAM Journal on Control and Optimization 60 (2), 758-785
Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizon
M. Basei, X. Guo, A. Hu, Y. Zhang, “Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizon”. Journal of Machine Learning Research, 23 (178), 1-34
A General Framework for Learning Mean-Field Games
Guo, X., Hu, A., Xu, R., & Zhang, J. (2022). A General Framework for Learning Mean-Field Games. Mathematics of Operations Research. https://doi.org/10.1287/moor.2022.1274