Research

2025

• Learning bounds for doubly-robust covariate shift adaptation.
  Jeonghwan Lee and Cong Ma.
  

  Submitted to the 37th International Conference on Algorithmic Learning Theory (ALT 2026). (arXiv) (PDF)

2024

• Off-policy estimation with adaptively collected data: the power of online learning.
  Jeonghwan Lee and Cong Ma.
  

  The 38th Annual Conference on Neural Information Processing Systems (NeurIPS), Dec. 2024. (arXiv) (PDF)
  

• A Worker-Task Specialization Model for Crowdsourcing: Efficient Inference and Fundamental Limits.
  Doyeon Kim*, Jeonghwan Lee*, and Hye Won Chung.
  

  IEEE Transactions on Information Theory, vol. 70, no. 3, pp. 2076-2117, Mar. 2024. (arXiv) (PDF)
  

2022

• A Generalized Worker-Task Specialization Model for Crowdsourcing: Optimal Limits and Algorithm.
  Doyeon Kim*, Jeonghwan Lee*, and Hye Won Chung.
  

  The proceedings of IEEE International Symposium on Information Theory (ISIT), pp. 1483-1488, Jul. 2022. (PDF)

2020

• Robust Hypergraph Clustering via Convex Relaxation of Truncated MLE.
  Jeonghwan Lee, Daesung Kim, and Hye Won Chung.
  

  IEEE Journal On Selected Areas in Information Theory (JSAIT), vol. 1, no. 3, pp. 613-631, Nov. 2020. (arXiv) (PDF)

* denotes either the equal contribution or the alphabetical ordering.