26 citations · 39 across the 21 of their papers we have counts for
21 papers
Learning Sequential Mobility Choice: A Review of Route and Activity Choice through Inverse Reinforcement Learning and Imitation Learning
Hung Tran, Tien Mai, Viet Bui
Route and activity choice are distinct transportation problems that both require models of feasible decisions unfolding over networks and time. This critical integrative review con…
Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Competitive Games
The Viet Bui, Tien Mai, Thanh Hong Nguyen
Training agents in multi-agent competitive games presents significant challenges due to their intricate nature. These challenges are exacerbated by dynamics influenced not only by…
Network-based Representations and Dynamic Discrete Choice Models for Multiple Discrete Choice Analysis
Hung Tran, Tien Mai
In many choice modeling applications, people demand is frequently characterized as multiple discrete, which means that people choose multiple items simultaneously. The analysis and…
Constrained Assortment Optimization under the Cross-Nested Logit Model
Cuong Le, Tien Mai
We study the assortment optimization problem under general linear constraints, where the customer choice behavior is captured by the Cross-Nested Logit model. In this problem, ther…
Tackling Stackelberg Network Interdiction against a Boundedly Rational Adversary
Tien Mai, Avinandan Bose, Arunesh Sinha +1
This work studies Stackelberg network interdiction games -- an important class of games in which a defender first allocates (randomized) defense resources to a set of critical node…
Solving Richly Constrained Reinforcement Learning through State Augmentation and Reward Penalties
Hao Jiang, Tien Mai, Pradeep Varakantham +1
Constrained Reinforcement Learning has been employed to enforce safety constraints on policy through the use of expected cost constraints. The key challenge is in handling expected…
Joint Binary-Continuous Fractional Programming: Solution Methods and Applications
Hoang Giang Pham, Ngan Ha Duong, Tien Mai +2
In this paper, we investigate a class of non-convex sum-of-ratios programs relevant to decision-making in key areas such as product assortment and pricing, and facility location an…
Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games
The Viet Bui, Tien Mai, Thanh H. Nguyen
Recent research on vulnerabilities of deep reinforcement learning (RL) has shown that adversarial policies adopted by an adversary agent can influence a target RL agent (victim age…
Weighted Maximum Entropy Inverse Reinforcement Learning
The Viet Bui, Tien Mai, Patrick Jaillet
We study inverse reinforcement learning (IRL) and imitation learning (IM), the problems of recovering a reward or policy function from expert's demonstrated trajectories. We propos…
Scalable Distributional Robustness in a Class of Non Convex Optimization with Guarantees
Avinandan Bose, Arunesh Sinha, Tien Mai
Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO so…
Joint Location and Cost Planning in Maximum Capture Facility Location under Multiplicative Random Utility Maximization
Ngan Ha Duong, Tien Thanh Dam, Thuy Anh Ta +1
We study a joint facility location and cost planning problem in a competitive market under random utility maximization (RUM) models. The objective is to locate new facilities and m…
Estimation of Recursive Route Choice Models with Incomplete Trip Observations
Tien Mai, The Viet Bui, Quoc Phong Nguyen +1
This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i.e., there are unconnected links (or nodes) in the o…
Safe Delivery of Critical Services in Areas with Volatile Security Situation via a Stackelberg Game Approach
Tien Mai, Arunesh Sinha
Vaccine delivery in under-resourced locations with security risks is not just challenging but also life threatening. The current COVID pandemic and the need to vaccinate have added…
Robust Entropy-regularized Markov Decision Processes
Tien Mai, Patrick Jaillet
Stochastic and soft optimal policies resulting from entropy-regularized Markov decision processes (ER-MDP) are desirable for exploration and imitation learning applications. Motiva…
Robust Maximum Capture Facility Location under Random Utility Maximization Models
Anh Thuy Ta, Tien Thanh Dam, Tien Mai
We study a robust version of the maximum capture facility location problem in a competitive market, assuming that each customer chooses among all available facilities according to…
Submodularity and Local Search Approaches for Maximum Capture Problems under Generalized Extreme Value Models
Tien Thanh Dam, Thuy Anh Ta, Tien Mai
We study the maximum capture problem in facility location under random utility models, i.e., the problem of seeking to locate new facilities in a competitive market such that the c…
A Relation Analysis of Markov Decision Process Frameworks
Tien Mai, Patrick Jaillet
We study the relation between different Markov Decision Process (MDP) frameworks in the machine learning and econometrics literatures, including the standard MDP, the entropy and g…
Modeling Route Choice with Real-Time Information: Comparing the Recursive and Non-Recursive Models
Xinlian Yu, Tien Mai, Jing Ding-Mastera +2
We study the routing policy choice problems in a stochastic time-dependent (STD) network. A routing policy is defined as a decision rule applied at the end of each link that maps t…
Robust Product-line Pricing under Generalized Extreme Value Models
Tien Mai, Patrick Jaillet
We study robust versions of pricing problems where customers choose products according to a generalized extreme value (GEV) choice model, and the choice parameters are not known ex…
Inverse Reinforcement Learning with Missing Data
Tien Mai, Quoc Phong Nguyen, Kian Hsiang Low +1
We consider the problem of recovering an expert's reward function with inverse reinforcement learning (IRL) when there are missing/incomplete state-action pairs or observations in…
Generalized Maximum Causal Entropy for Inverse Reinforcement Learning
Tien Mai, Kennard Chan, Patrick Jaillet
We consider the problem of learning from demonstrated trajectories with inverse reinforcement learning (IRL). Motivated by a limitation of the classical maximum entropy model in ca…