1 citations · 2 across the 15 of their papers we have counts for
16 papers
Latent Representation Alignment for Offline Goal-Conditioned Reinforcement Learning
Hyungkyu Kang, Byeongchan Kim, Min-hwan Oh
Offline goal-conditioned reinforcement learning (GCRL) provides a practical framework for obtaining goal-reaching policies from fixed datasets. However, learning a reliable goal-co…
Nonstationary Generalized Linear Bandits with Discounted Online Mirror Descent
Joongkyu Lee, Min-hwan Oh
We study nonstationary generalized linear bandits (GLBs), where the expected reward is modeled through a nonlinear link function with an unknown time-varying parameter. This framew…
Multi-Step Likelihood-Ratio Correction for Reinforcement Learning with Verifiable Rewards
Deokgyu Yoon, Hyungkyu Kang, Joongkyu Lee +4
Reinforcement learning with verifiable rewards (RLVR) plays a pivotal role in improving the reasoning ability of large language models. However, widely used PPO surrogate objective…
Block-Sphere Vector Quantization
Heesang Ann, Joongkyu Lee, Min-hwan Oh
Vector quantization is a fundamental primitive for scalable machine learning systems, enabling memory-efficient storage, fast retrieval, and compressed inference. Recent rotation-b…
Peng's Q() for Conservative Value Estimation in Offline Reinforcement Learning
Byeongchan Kim, Min-hwan Oh
We propose a model-free offline multi-step reinforcement learning (RL) algorithm, Conservative Peng's Q() (CPQL). Our algorithm adapts the Peng's Q() (PQL) operator for conse…
RelFlexformer: Efficient Attention 3D-Transformers for Integrable Relative Positional Encodings
Byeongchan Kim, Arijit Sehanobish, Avinava Dubey +2
We present a new class of efficient attention mechanisms applying universal 3D Relative Positional Encoding (RPE) methods given by arbitrary integrable modulation functions . Th…
Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification
Joongkyu Lee, Min-hwan Oh
We study optimal experimental design for multinomial logit (MNL) bandits, where an agent repeatedly selects a subset of items from a ground set of size and observes single-…
Exploration via Feature Perturbation in Contextual Bandits
Seouh-won Yi, Min-hwan Oh
We propose feature perturbation, a simple yet effective exploration strategy for contextual bandits that injects randomness directly into feature inputs, instead of randomizing unk…
Preference-based Reinforcement Learning beyond Pairwise Comparisons: Benefits of Multiple Options
Joongkyu Lee, Seouh-won Yi, Min-hwan Oh
We study online preference-based reinforcement learning (PbRL) with the goal of improving sample efficiency. While a growing body of theoretical work has emerged-motivated by PbRL'…
Combinatorial Reinforcement Learning with Preference Feedback
Joongkyu Lee, Min-hwan Oh
In this paper, we consider combinatorial reinforcement learning with preference feedback, where a learning agent sequentially offers an action--an assortment of multiple items to--…
Improved Online Confidence Bounds for Multinomial Logistic Bandits
Joongkyu Lee, Min-hwan Oh
In this paper, we propose an improved online confidence bound for multinomial logistic (MNL) models and apply this result to MNL bandits, achieving variance-dependent optimal regre…
Demystifying Linear MDPs and Novel Dynamics Aggregation Framework
Joongkyu Lee, Min-hwan Oh
In this work, we prove that, in linear MDPs, the feature dimension is lower bounded by in order to aptly represent transition probabilities, where is the size of the…
Lasso Bandit with Compatibility Condition on Optimal Arm
Harin Lee, Taehyun Hwang, Min-hwan Oh
We consider a stochastic sparse linear bandit problem where only a sparse subset of context features affects the expected reward function, i.e., the unknown reward parameter has a…
Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
Wooseong Cho, Taehyun Hwang, Joongkyu Lee +1
We study reinforcement learning with multinomial logistic (MNL) function approximation where the underlying transition probability kernel of the Markov decision processes (MDPs) is…
Nearly Minimax Optimal Regret for Multinomial Logistic Bandit
Joongkyu Lee, Min-hwan Oh
In this paper, we study the contextual multinomial logit (MNL) bandit problem in which a learning agent sequentially selects an assortment based on contextual information, and user…
Learning Uncertainty-Aware Temporally-Extended Actions
Joongkyu Lee, Seung Joon Park, Yunhao Tang +1
In reinforcement learning, temporal abstraction in the action space, exemplified by action repetition, is a technique to facilitate policy learning through extended actions. Howeve…