Papers by Substantive Topics
Ad Optimization
"TextBO: Bayesian Optimization in Language Space for Eval-Efficient Self-Improving AI", by Enoch H. Kang* and Hema Yoganarasimhan, 2025
Consumer Analytics
"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)
Platform Experimentation
"LLM Personas as a Substitute for Field Experiments in Method Benchmarking", by Enoch H. Kang, 2025
"Is O (log N) practical? Near-Equivalence Between Delay Robustness and Bounded Regret in Bandits and RL", by Enoch H. Kang* and P. R. Kumar, NeurIPS 2024
Ad Optimization
"TextBO: Bayesian Optimization in Language Space for Eval-Efficient Self-Improving AI", by Enoch H. Kang* and Hema Yoganarasimhan, 2025
"Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle", Enoch H Kang, P. R. Kumar, Allerton 2023
"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
"Reasonably Reasoning AI Agents Avoid Game-Theoretic Failures in Zero-Shot, Provably" by Enoch H. Kang, 2026
"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
Papers by Methodology
Self-improving AI agent
"TextBO: Bayesian Optimization in Language Space for Eval-Efficient Self-Improving AI", by Enoch H. Kang* and Hema Yoganarasimhan, 2025
"Reasonably Reasoning AI Agents Avoid Game-Theoretic Failures in Zero-Shot, Provably" by Enoch H. Kang, 2026
Online (iterative) alignment
"Demystifying the Unreasonable Effectiveness of Online Alignment Methods", by Enoch Hyunwook Kang, 2026
"LLM Personas as a Substitute for Field Experiments in Method Benchmarking", by Enoch H. Kang, 2025
"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)
"Reasonably Reasoning AI Agents Avoid Game-Theoretic Failures in Zero-Shot, Provably" by Enoch H. Kang, 2026
"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)
"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
"TextBO: Bayesian Optimization in Language Space for Eval-Efficient Self-Improving AI", by Enoch H. Kang* and Hema Yoganarasimhan, 2025
"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
"Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle", Enoch H Kang, P. R. Kumar, Allerton 2023