1 citations · 2 across the 9 of their papers we have counts for
9 papers
A Lecture Note on Offline RL and IRL, Part II: Foundations of Inverse Reinforcement Learning and Dynamic Discrete Choice Models
Enoch Hyunwook Kang
In the forward reinforcement-learning problem, the reward is fixed and known; the learner is asked to find a good policy or value function. Here we turn the question around. Given…
Personalized Alignment Revisited: The Necessity and Sufficiency of User Diversity
Enoch Hyunwook Kang
Personalized alignment aims to adapt large language models to heterogeneous user preferences, yet the precise theoretical conditions for its statistical efficiency have not been fo…
Reasonably reasoning AI agents can avoid game-theoretic failures in zero-shot, provably
Enoch Hyunwook Kang
As autonomous AI agents increasingly mediate online platform markets, a fundamental question emerges: do these markets generate stable strategic outcomes? In repeated strategic env…
Demystifying the Unreasonable Effectiveness of Greedy Alignment Methods
Enoch Hyunwook Kang
Greedy AI alignment methods based on Bradley-Terry reward-model fitting and KL-regularized Alignment are remarkably effective in both offline and online preference-learning pipelin…
LLM Personas as a Substitute for Field Experiments in Method Benchmarking
Enoch Hyunwook Kang
Field experiments (A/B tests) are often the most credible benchmark for methods (algorithms) in societal systems, but their cost and latency bottleneck rapid methodological progres…
TextBO: Bayesian Optimization in Language Space for Eval-Efficient Self-Improving AI
Enoch Hyunwook Kang, Hema Yoganarasimhan
Large Language Models (LLMs) have enabled self-improving AI systems that iteratively generate, evaluate, and refine their outcomes. Recent studies show that prompt-optimization-bas…
Stability and Generalization for Bellman Residuals
Enoch H. Kang, Kyoungseok Jang
Offline reinforcement learning and offline inverse reinforcement learning aim to recover near-optimal value functions or reward models from a fixed batch of logged trajectories, ye…
An Empirical Risk Minimization Approach for Offline Inverse RL and Dynamic Discrete Choice Model
Enoch H. Kang, Hema Yoganarasimhan, Lalit Jain
We study the problem of estimating Dynamic Discrete Choice (DDC) models, also known as offline Maximum Entropy-Regularized Inverse Reinforcement Learning (offline MaxEnt-IRL) in ma…
Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle
Enoch Hyunwook Kang, P. R. Kumar
In online exploration systems where users with fixed preferences repeatedly arrive, it has recently been shown that O(1), i.e., bounded regret, can be achieved when the system is m…