Research

Publications

1. [J-2025] Pham H.G*, Mai T. Constrained Assortment Optimization under the Mixed-Logit Model: Approximation Schemes and Outer Approximation Approaches, European Journal of Operational Research (2025). [Discrete Choice] [Revenue Mangement] [pdf]

2. [J-2025] Shao Q.*, Mai T., and Cheng Shih-Fen. Constrained Pricing in Logit-based Revenue Management (INFORMS Journal on Computing).  [Discrete Choice] [Revenue Mangement] [pdf]

3. [C-2025]  Hoang H*., Mai T., Varakantham P. No Experts, No Problem: Avoidance Learning from Bad Demonstrations (NeurIPS 2025). [Imitation Learning][IRL/RL][pdf]

4. [C-2025]  Pham Q.A.*, Brahmanage J.C.*, Mai T., Kumar A., IOSTOM: Offline Imitation Learning from Observations via State Transition Occupancy Matching (NeurIPS 2025).  [Imitation Learning][IRL/RL]

5. [C-2025]  Bui T.V.*, Mai T., Thanh H. Nguyen, MisoDICE: Multi-Agent Imitation from Mixed-Quality Demonstrations (NeurIPS 2025). [IRL/RL] [Multi-agent Reinforcement Learning][Competitive Games][pdf]

6. [J-2025] Pham H.G*, Ta T.A., Mai T., An Exponential Cone Integer Programming Approach for 0-1 Fractional Optimization ( Journal of Combinatorial Optimization) [Discrete Choice] [Revenue Mangement].

7. [C-2025] Bui T.V.*, Mai T., Thanh H. Nguyen, O-MAPL: Offline Multi-agent Preference Learning (ICML 2025) [IRL/RL] [Multi-agent Reinforcement Learning][Competitive Games][pdf]

8. [C-2025] Bui T.V.*, Thanh H. Nguyen, Mai T., ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization (ICLR 2025) [IRL/RL] [Imitation Learning][Competitive Games][pdf]

9. [J-2024] Le C.*, Mai T., Constrained Assortment Optimization under the Cross-Nested Logit Model. Production and Operations Management (2024)[Discrete Choice] [Revenue Mangement] [pdf] [link]

10. [J-2024] Tran H.*, Mai T., Network-based Representations and Dynamic Discrete Choice Models for Multiple Discrete Choice Analysis. Transportation Research Part B (2024) [Discrete Choice] [Route Choice] [pdf]

11. [C-2024] Bui T.V.*,  Mai T., Thanh H. Nguyen, Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Competitive Games. In Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS 2024). [IRL/RL] [Imitation Learning][Competitive Games] [pdf]

12. [C-2024] Hoang H*., Mai T., Varakantham P. SPRINQL: Sub-optimal Demonstrations driven Offline Imitation Learning. In  Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS 2024). [Imitation Learning][IRL/RL][pdf]

13. [C-2024] Bui T.V.*,  Mai T., Thanh H. Nguyen, Inverse Factorized Q-Learning for Cooperative Multi-agent Imitation Learning. In Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS 2024)  [IRL/RL] [Imitation Learning][Multiagent Games] [pdf]

14. [C-2024] Mai T., Bose A., Sinha A., Thanh H. Nguyen, Stackelberg Network Interdiction under Boundedly Rational Adversary. In Proceeding of the   33rd International Joint Conference on Artificial Intelligence (IJCAI2024). [Dynamic Discrete Choice] [Behavioral game theory][pdf]

15. [C-2024] Jiang H.*, Mai T., Varakantham P., Hoang H*, Solving Constrained Reinforcement Learning through Augmented State and Reward Penalties, in Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI2024 - Acceptance rate: 23.75%) [IRL/RL][pdf]

16. [C-2024] Hoang H*., Mai T., Varakantham P. Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learning, in Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI2024 - Acceptance rate: 23.75%) . [Imitation Learning][IRL/RL][pdf]

17. [J-2023] Ngan H.*, Dam T.T.*, Ta T.A., Mai T.  Joint Location and Cost Planning in Maximum Capture Problems under Random Utility Models. Computer and Operations Research (2023). [Discrete Choice] [Facility Location] [pdf]

18. [J-2023] Dam T.T*., Ta. T.A, Mai T.  Robust Maximum Capture Facility Location under Random Utility Models. European Journal of Operational Research (2023). [Discrete Choice] [Facility Location] [pdf]

19. [C-2023] Bui T.V.*, Mai T., Imitation Improvement Learning for Large-scale Capacitated Vehicle Routing Problems, in Proceedings of the 33rd International Conference on Automated Planning and Scheduling (ICAPS 2023) [Imitation Learning]

20. [C-2023] Bui T.V.*, Mai T., Thanh H. Nguyen, Imitating Opponent to Win: Adversarial Policy Imitation Learn- ing in Two-player Competitive Games, in Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023).

21. [C-2023] Bose A.*, Li T., Sinha A., Mai T., A Fair Incentive Scheme for Community Health Workers, in Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI 2023).

22. [C-2023] Mai T., Sinha A., Safe Delivery of Critical Services in Areas with Volatile Security Situation via a Stackelberg Game Approach, in Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI 2023).

23. [J-2023] Mai T., Bui T.V.*, Nguyen Q.P., Le T.V., Estimation of Recursive Route Choice Models with Incomplete Trip Observations. Transportation Research Part B (2023) [Discrete Choice] [Route Choice] [pdf]

24. [B-2023] Mai T., Lodi A., An algorithm for assortment optimization under parametric discrete choice models. Fields Institute Communications Series on Data Science and Optimization (2023). [Discrete Choice] [Revenue Mangement] [pdf]

25. [J-2022] Mai T., Frejinger E. Undiscounted Recursive Path Choice Models: Convergence Properties and Algorithms. Transportation Science (2022).  [Discrete Choice] [Route Choice] [link] [pdf]

26. [J-2022] Dam T.T.*, Ta T.A., Mai T., Joint Chance-constrained Simulation-based Staffing Optimization in Multi-skill Call Centers, Journal of Combinatorial Optimization (2022). [Discrete Optimization] [Stochastic Simulation]

27. [C-2022] Bose A.*, Sinha A., Mai T., Scalable Distributional Robustness in a Class of Non-Convex Optimization with Guarantees, in Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) (Acceptance rate: 25.6%)[Discrete Choice] [Robust Optimization] [pdf]

28. [C-2022] Ta T.A., Mai T., Bastin F., l'Ecuyer P. A logistic regression and linear programming approach for multi-skill staffing optimization in call centers,  in Proceedings of the Winter Simulation Conference 2022, Singapore (WSC2022) Singapore [Discrete Optimization] [Simulation] [pdf]

29. [C-2022] Mai T., Sinha A., Choices Are Not Independent: Stackelberg Security Games with Nested Quantal Response Models, in Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI2022) (Acceptance rate: 1349/9020 = 15%). [Discrete Choice] [Nested logit] [Security Game] [pdf]

30. [J-2021] Dam T.T.*, Ta T.A., Mai T., Submodularity and Local Search Approaches for Maximum Capture Problems under Generalized Extreme Value Models. European Journal of Operational Research (2021). [Discrete Choice] [Facility Location] [pdf]

31. [J-2021] Mai T., Yu X., Gao S., Frejinger E., Route choice in a stochastic time-dependent network: the recursive model and solution algorithm. Transportation Research Part B, Volume 151, (2021). [Discrete Choice] [Route Choice] [pdf]

32. [J-2020] Ta T.A., Mai T., Bastin F., l'Ecuyer P., On a multistage discrete stochastic optimization problem with stochastic constraints and nested sampling, Mathematical Programming (2020). [Stochastic Programming] [Stochastic Simulation] [pdf]

33. [J-2020] de Moraes Ramos G., Mai T., Daamen W., Frejinger E., Hoogendoorn S. P. Route choice behaviour and travel information in a congested network: Static and dynamic recursive models. Transportation Research Part C: Emerging Technologies (2020) 114, 681-693. [Discrete Choice] [Route Choice] [pdf]

34. [J-2020] Mai T., Lodi A. A multicut outer-approximation approach for competitive facility location under random utilities. European Journal of Operational Research 284.3 (2020): 874-881. [Discrete Choice] [Facility Location] [pdf]

35. [J-2028] Bastin F., Liu Y., Cirillo C., Mai T., Transferring Time-Series Discrete Choice to Link-based Route Choice in Space: Estimating Vehicle Type Preference Using Recursive Logit Model , Transportation Research Record (2018). [Discrete Choice] 

36. [J-2017] Mai T., Frejinger E., Fosgerau M., Bastin F., A dynamic programming approach for quickly estimating large network-based MEV models, Transportation Research Part B: Methodological 98 (2017): 179-197. [Discrete Choice] [pdf]

37. [J-2017] Zimmermann M., Mai T., Frejinger E. Bike route choice modeling using GPS data without choice sets of paths, Transportation Research Part C: Emerging Technologies 75 (2017): 183-196. [Discrete Choice] [Route Choice]

38. [J-2017] Mai T. , Frejinger, E., Bastin, F. On the similarities between random regret minimization and mother logit: the case of recursive route choice models, Journal of Choice Modeling (2017). [Discrete Choice] [Route Choice]

39. [J-2017] Mai T., A method of integrating correlation structures for a generalized recursive route choice model, Transportation Research Part B, 93(1):p.146-161, 2016. [Discrete Choice] [Route Choice] [pdf]

40. [J-2015] Mai T., Fosgereau M., Frejinger E., A nested recursive logit for route choice analysis, Transportation Research Part B, 71(1):p.100-112, (2015).  [Discrete Choice] [Route Choice] [pdf]

41. [J-2015] Mai T., Frejinger, E., Bastin, F., A misspecification test for logit-based route choice models, Economics of Transportation, 4(4):p.215-226, (2015). [Discrete Choice] [Route Choice]

42. [J-2015] Mai T., Bastin F., Frejinger E., A decomposition method for estimating complex recursive logit-based route choice models, EURO Journal on Transportation and Logistics (2015): 1-23. [Discrete Choice] [Route Choice] [pdf]

43. [C-2020] Barabonkovy D.*, D'Alonzoy S.*, Pierre J.*, Kondor D., Zhang X., Mai T. (2020). Simulating and evaluating rebalancing strategies for dockless Bike-Sharing Systems, in Proceedings of the Transportation Research Board (TRB) 99th Annual Meeting, January 12--16.

[J]: Journal publications, [C]: Conference proceedings, [B]: Book chapters

1. Tran H.*,  Mai T., Equilibrium-Constrained Estimation of Recursive Logit Choice Models . [Discrete Choice] [Route Choice][pdf]

2. Bui T.V.*, Mai T., Thanh H. Nguyen. From Scarcity to Efficiency: Preference-Guided Learning for Sparse-Reward Multi-Agent Reinforcement Learning [Multi-agent RL] .[pdf]

3. Pham H.G*, Mai T. , Ha M.H, On the Estimation of Multinomial Logit and Nested Logit Models: A Conic Optimization Approach (submitted). [Discrete Choice] [pdf]

4. Tran H.*,  Mai T. , Ha M.H, Constrained Recursive Logit for Route Choice Analysis (submitted). [Discrete Choice] [Route Choice][pdf]

5. Hoang H*., Mai T., Varakantham, Verma T., Learning What to Do and What Not To Do: Offline Imitation from Expert and Undesirable Demonstrations [Imitation Learning][IRL/RL]

6. Le C.*, Mai T., Duong N.H.*, Ha M.H., Competitive Facility Location with  Market Expansion and Customer-centric Objective (under review) [Discrete Choice] [Facility Location] [pdf]

7. Le B.L.*, Mai T., Ta T.A, Ha M.H., Vu D.M, Competitive Facility Location under Cross-Nested Logit Customer Choice Model: Hardness and Exact Approaches (submitted) [Discrete Choice] [Facility Location] [pdf]

8. Pham H.G*, Dam T.T*, Duong N.H.*, Mai T., Ha M.H., Competitive Facility Location under  Random Utilities and Routing Constraints (submitted). [Discrete Choice] [Facility Location] [pdf]

9. Bui T.V.*,  Mai T., Thanh H. Nguyen, Unleashing Imitation Learning Potential in Two-Player Competitive Games  (submitted). [IRL/RL] [Imitation Learning][Competitive Games] (Extended journal version of our  AAMAS23 paper).

10. Pham H.G*, Duong N.H.*, Mai T., Ta T.A., Ha M.H. Joint Binary-Continuous Fractional Programming: Solution Methods and Applications (submitted) [Discrete Choice] [Combinatorial optimization] [pdf]

11. Bui T.V.*, Mai T., Jaillet P., Weighted Maximum Entropy Inverse Reinforcement Learning. [Behavior Learning] [IRL] [pdf]

12. Mai T., Jaillet P., Robust pricing under generalized extreme value models. [Discrete Choice] [Revenue Mangement] [pdf]

13. Mai T., Jaillet P., On the relation between Markov Decision Process frameworks. [IRL/RL]  [pdf]

14. Mai T., Jaillet P., Robust entropy-regularized Markov Decision Processes.  [IRL/RL] [pdf]

15. Mai T., Nguyen Q. P., Low K. H., Jaillet P., Inverse reinforcement learning with missing data. [IRL/RL] [Route Choice]  [pdf]

(*) indicate student or research engineer under my supervision.

1. Mai T. (2016). Dynamic programming approaches for estimating and applying large-scale discrete choice models, Ph.D. thesis, University of Montreal.

2. Mai T. (2013). Revisiting optimization algorithms for maximum likelihood estimation, Master thesis, University of Montreal.