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
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
Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods
Guo, X., Hu, A., & Zhang, J. (2022). Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods. Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), 6774-6782. https://doi.org/10.1609/aaai.v36i6.20633
Transaction cost analytics for corporate bonds
X. Guo, C.A. Lehalle, R. Xu, “Transaction cost analytics for corporate bonds”. Quantitative Finance, 2022.
MFGs for partially reversible investment
H. Cao, X. Guo, “MFGs for partially reversible investment”. Stochastic Processes and their Applications, 150, 995-1014.
Entropy regularization for mean field games with learning
X. Guo, R. Xu, T. Zariphopoulou, “Entropy regularization for mean field games with learning”. Mathematics of Operations Research 47 (4), 3239-3260
On the convex hull of convex quadratic optimization problems with indicators
Wei, Linchuan & Atamturk, Alper & Gomez, Andres & Küçükyavuz, Simge. (2023). On the convex hull of convex quadratic optimization problems with indicators. Mathematical Programming. 204. 10.1007/s10107-023-01982-0.