You can also find my articles on my Google Scholar profile.
Conference Publications
16. Optimistic Whittle Index Policy: Online Learning for Restless Bandits
Kai Wang*, Lily Xu*, Aparna Taneja, Milind Tambe
Under review
15. Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Care Domain
Kai Wang*, Shresth Verma*, Aditya Mate, Sanket Shah, Aparna Taneja, Neha Madhiwalla, Aparna Hegde, Milind Tambe
Under review
14. Smoothed Online Combinatorial Optimization Using Imperfect Predictions
Kai Wang, Zhao Song, Georgios Theocharous, Sridhar Mahadevan
Under review
13. Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses
Sanket Shah, Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe
NeurIPS 2022
12. Coordinating Followers to Reach Better Equilibria: End-to-End Gradient Descent for Stackelberg Games
Kai Wang, Lily Xu, Andrew Perrault, Michael K. Reiter, and Milind Tambe
AAAI 2022
11. Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning
Kai Wang, Sanket Shah, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, and Milind Tambe
NeurIPS 2021 (spotlight presentation)
10. Dual-Mandate Patrols: Multi-Armed Bandits for Green Security
Lily Xu, Elizabeth Bondi, Fei Fang, Andrew Perrault, Kai Wang, and Milind Tambe
AAAI 2021 (best paper runner up)
9. Automatically Learning Compact Quality-aware Surrogates for Optimization Problems
Kai Wang, Bryan Wilder, Andrew Perrault, and Milind Tambe
NeurIPS 2020 (spotlight presentation)
8. Robust Spatial-Temporal Incident Prediction
Ayan Mukhopadhyay, Kai Wang, Andrew Perrault, Mykel Kochenderfer, Milind Tambe, and Yevgeniy Vorobeychik
UAI 2020
7. Scalable Game-Focused Learning of Adversary Models:Data-to-Decisions in Network Security Games
Kai Wang, Andrew Perrault, Aditya Mate, and Milind Tambe
AAMAS 2020
6. DeepFP for Finding Approximate Nash Equilibrium in Continuous Action Spaces
Nitin Kamra, Umang Gupta, Kai Wang, Fei Fang, Yan Liu, and Milind Tambe
GameSec 2019
5. Learning to Signal in the Goldilocks Zone: Improving Adversary Compliance in Security Games
Sarah Cooney, Kai Wang, Elizabeth Bondi, Thanh Nguyen, Phebe Vayanos, Hailey Winetrobe, Edward Cranford, Cleotilde Gonzalez, Christian Lebiere, and Milind Tambe
ECML 2019
4. Deep Fictitious Play for Games with Continuous Action Spaces
Nitin Kamra, Umang Gupta, Kai Wang, Fei Fang, Yan Liu, and Milind Tambe
Extended abstract in AAMAS 2019
3. The Price of Usability: Designing Operationalizable Strategies for Security Games
Sara Marie Mc Carthy, Corine Laan, Kai Wang, Phebe Vayanos, Milind Tambe, and Arunesh Sinha
IJCAI 2018
2. Equilibrium Refinement in Security Games with Arbitrary Scheduling Constraints
Kai Wang, Qingyu Guo, Phebe Vayanos, Milind Tambe, and Bo An
AAMAS 2018
1. Strategic Coordination of Human Patrollers and Mobile Sensors with Signaling for Security Games
Haifeng Xu, Kai Wang, Phebe Vayanos, and Milind Tambe
AAAI 2018
Workshop Publication
7. Learning Opportunistic Adversarial Model on Global Wildlife Trade
Kai Wang, Jeffrey Brantingham, and Milind Tambe
AAMAS 2021 Autonomous Agents for Social Good Workshop (AASG)
6. Active Screening on Recurrent Diseases Contact Networks with Uncertainty: a Reinforcement Learning Approach
Han Ching Ou, Kai Wang, Finale Doshi-Velez, Milind Tambe AAMAS 2020 International Workshop on Multi-Agent Systems and Agent-Based Simulation
5. Balance Between Scalability and Optimality in Network Security Games
Kai Wang
AAMAS 2020 Doctoral Consortium
4. Adversarial Machine Learning with Double Oracle
Kai Wang, Bryan Wilder, and Milind Tambe
IJCAI 2019 Doctoral Consortium
3. Improving GP-UCB Algorithm by Harnessing Decomposed Feedback
Kai Wang, Bryan Wilder, Sze-chuan Suen, Milind Tambe, and Bistra Dilkina
ECML 2019 SoGood Workshop
2. Mobile Game Theory with Street Gangs
Sarah Cooney, Wendy Gomez, Kai Wang, Jorja Leap, P. Jeffery Brantingham, and Milind Tambe
ECML 2019 SoGood Workshop
1. Routing Games with Priorities
Kai Wang, Hong-Jyun Wang, and Ho-Lin Chen
AAAC 2016