2025

• Networked Restless Multi-Arm Bandits with Reinforcement Learning
  Hanmo Zhang, Kai Wang (PRL workshop AAAI 2025, in progress new!!)

• Soft Diffusion Actor-Critic: Efficient Online Reinforcement Learning for Diffusion Policy
  Haitong Ma, Tianyi Chen, Kai Wang, Li Na*, Bo Dai* (in submission, new!!)

• Primal-Dual Spectral Representation for Off-policy Evaluation
  Yang Hu, Tianyi Chen, Na Li, Kai Wang, Bo Dai (AISTATS 2025)

• 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!!)

• What’s in a Query: Polarity-aware Distribution-based Fair Ranking
  Aparna Balagopalan*, Kai Wang*, Olawale Elijah Salaudeen, Asia Biega, Marzyeh Ghassemi (WWW 2025)

2024

• 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)

• Fully First-Order Methods for Linearly Constrained Bilevel Optimization
  Guy Kornowski*, Swati Padmanabhan*, Kai Wang*, Zhe Zhang*, Suvrit Sra (NeurIPS 2024)

2023

• 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)

• 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)

• Optimistic Whittle Index Policy: Online Learning for Restless Bandits
  Kai Wang*, Lily Xu*, Aparna Taneja, Milind Tambe (AAAI 2023)

• 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)

• Smoothed Online Combinatorial Optimization Using Imperfect Predictions
  Kai Wang, Zhao Song, Georgios Theocharous, Sridhar Mahadevan (AAAI 2023)

2022

• Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses
  Sanket Shah, Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe (NeurIPS 2022)

• 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)

2021

• 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)

• 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)

2020

• Automatically Learning Compact Quality-aware Surrogates for Optimization Problems
  Kai Wang, Bryan Wilder, Andrew Perrault, and Milind Tambe (NeurIPS 2020 spotlight presentation)

• Robust Spatial-Temporal Incident Prediction
  Ayan Mukhopadhyay, Kai Wang, Andrew Perrault, Mykel Kochenderfer, Milind Tambe, and Yevgeniy Vorobeychik (UAI 2020)

• 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)

2019

• 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)

• 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)

• 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)

• Adversarial Machine Learning with Double Oracle
  Kai Wang, Bryan Wilder, and Milind Tambe (IJCAI 2019 Doctoral Consortium)

• 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)

2018

• 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)

• Equilibrium Refinement in Security Games with Arbitrary Scheduling Constraints
  Kai Wang, Qingyu Guo, Phebe Vayanos, Milind Tambe, and Bo An (AAMAS 2018)

• Strategic Coordination of Human Patrollers and Mobile Sensors with Signaling for Security Games
  Haifeng Xu, Kai Wang, Phebe Vayanos, and Milind Tambe (AAAI 2018)