Research

Below, you’ll find my writing organized by publication type and year. 

"Bayesian optimization in language space: An eval-efficient AI self-improvement framework", by Enoch H. Kang* and Hema Yoganarasimhan, 2025 

• Stanford AI & Marketing: New Methods and New Risks Conference

• Columbia AI/ML conference

"Stability and generalization for Bellman residuals", Enoch H. Kang and Kyoungseok Jang, 2025 

• This paper proves the first O(1/n) statistical convergence guarantee for gradient-based methods for Offline RL/IRL/Dynamic discrete choice models.

"Empirical risk minimization for Inverse RL and Dynamic Discrete Choice models" by Enoch H. Kang*, Lalit Jain, and Hema Yoganarasimhan, 2025 (minor revision, Operations Research)

• Accepted in the main track, ACM Economics and Computation (EC) 2025,

• Invited tutorial talk (with John Rust), Econometric Society Summer School in Dynamic Structural Econometrics 2025 (YouTube Link) (Slides)

• The 2025 World Congress of the Econometric Society (ESWC 2025)

• UBC econometric lunch seminar 

"Reasonably reasoning AI agents avoid game-theoretic failures in zero-shot, provably", working manuscript

"Debiasing causal machine learning via causal representations in language models", working manuscript

"Fast globally convergent gradient-based offline reinforcement learning", working manuscript 

"Is O (log N) practical? Near-Equivalence Between Delay Robustness and Bounded Regret in Bandits and RL", Enoch H. Kang* and P. R. Kumar, NeurIPS 2024, 

• Establishes the importance of customer diversity in dealing with experiments with reward delays

• NeurIPS 2023 Adaptive Experimentation and Active Learning in the Real World (ReALML) workshop 

"Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle", Enoch H Kang, P. R. Kumar,  Allerton 2023

• ICML 2022 Adaptive Experimentation and Active Learning in the Real World (ReALML) workshop 

• RecSys 2022 Causality, Counterfactuals, Sequential Decision-Making & Reinforcement Learning workshop (Selected as the Long Oral presentation)

"Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning", Enoch H. Kang, Taehwan Kwon, James R. Morrison, and Jinkyoo Park, NeurIPS 2022

• ICML GRL+: novel applications, best paper runner up (Oral presentation link)