Publications
Research interests: AI for social impact, machine learning, optimization, online learning, algorithmic game theory
Conference publications
23. Primal-Dual Spectral Representation for Off-policy Evaluation
Yang Hu, Tianyi Chen, Na Li, Kai Wang, Bo Dai (in submission, new!!)
22. What is the Right Notion of Distance between Predict-then-Optimize Tasks?
Paula Rodriguez-Diaz, Lingkai Kong, Kai Wang, David Alvarez-Melis, Milind Tambe (in submission, new!!)
21. What's in a Query: Examining Distribution-based Amortized Fair Ranking
Aparna Balagopalan, Kai Wang, Asia Biega, Marzyeh Ghassemi (in submission, new!!)
20. Aligning Large Language Models with Representation Editing: A Control Perspective
Lingkai Kong, Haorui Wang, Wenhao Mu, Yuanqi Du, Yuchen Zhuang, Yifei Zhou, Yue Song, Rongzhi Zhang, Kai Wang, Chao Zhang (NeurIPS 2024)
19. Fully First-Order Methods for Linearly Constrained Bilevel Optimization
Guy Kornowski*, Swati Padmanabhan*, Kai Wang*, Zhe Zhang*, Suvrit Sra (NeurIPS 2024)
18. Characterizing and Improving the Robustness of Predict-Then-Optimize Frameworks
Sonja Johnson-Yu, Jessica Finocchiaro, Arunesh Sinha, Kai Wang, Yevgeniy Vorobeychik, Aparna Taneja, Milind Tambe (GameSec 2023)
17. Restless Multi-Armed Bandits for Maternal and Child Health: Results from Decision-Focused Learning
Shresth Verma, Aditya Mate, Kai Wang, Neha Madhiwalla, Aparna Hegde, Aparna Taneja, Milind Tambe (AAMAS 2023)
16. Optimistic Whittle Index Policy: Online Learning for Restless Bandits
Kai Wang*, Lily Xu*, Aparna Taneja, Milind Tambe (AAAI 2023)
15. Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health
Kai Wang*, Shresth Verma*, Aditya Mate, Sanket Shah, Aparna Taneja, Neha Madhiwalla, Aparna Hegde, Milind Tambe (AAAI 2023)
14. Smoothed Online Combinatorial Optimization Using Imperfect Predictions
Kai Wang, Zhao Song, Georgios Theocharous, Sridhar Mahadevan (AAAI 2023)
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 publications
8. What is the Right Notion of Distance between Predict-then-Optimize Tasks?
Paula Rodriguez-Diaz, Kai Wang, David Alvarez-Melis, Milind Tambe (ICML 2024 Workshop on Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact)
7. Case Study: Applying Decision Focused Learning in the Real World
Shresth Verma, Aditya Mate, Kai Wang, Aparna Taneja, and Milind Tambe (NeurIPS 2022 Workshop on Trustworthy and Socially Responsible Machine Learning)
6. Learning Opportunistic Adversarial Model on Global Wildlife Trade
Kai Wang, Jeffrey Brantingham, and Milind Tambe (AAMAS 2021 Autonomous Agents for Social Good Workshop)
5. 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)
4. Balance Between Scalability and Optimality in Network Security Games
Kai Wang (AAMAS 2020 Doctoral Consortium)
3. Adversarial Machine Learning with Double Oracle
Kai Wang, Bryan Wilder, and Milind Tambe (IJCAI 2019 Doctoral Consortium)
2. Improving GP-UCB Algorithm by Harnessing Decomposed Feedback
Kai Wang, Bryan Wilder, Sze-chuan Suen, Milind Tambe, and Bistra Dilkina (ECML 2019 SoGood Workshop; also appeared in the book of “Machine Learning and Knowledge Discovery in Databases”, in proceedings)
1. Routing Games with Priorities
Kai Wang, Hong-Jyun Wang, and Ho-Lin Chen (AAAC 2016)