2025

  • [C27] FairDICE: Fairness-Driven Offline Multi-Objective Reinforcement Learning
    Woosung Kim*, Jinho Lee*, Jongmin Lee^, Byung-Jun Lee^
    NeurIPS 2025
    paper
  • [C26] SEMDICE: Off-policy State Entropy Maximization via Stationary Distribution Correction Estimation
    Jongmin Lee*, Meiqi Sun*, Pieter Abbeel
    ICLR 2025
    paper code

2024

  • [C23] Mitigating Covariate Shift in Behavioral Cloning via Robust Distribution Correction Estimation
    Seokin Seo, Byung-Jun Lee, Jongmin Lee, HyeongJoo Hwang, Hongseok Yang, Kee-Eung Kim
    NeurIPS 2024
    paper
  • [C22] ROIDICE: Offline Return on Investment Maximization for Efficient Decision Making
    Woosung Kim*, Hayeong Lee*, Jongmin Lee^, Byung-Jun Lee^
    NeurIPS 2024
    paper
  • [C25] Body Transformer: Leveraging Robot Embodiment for Policy Learning
    Carmelo Sferrazza, Dun-Ming Huang, Fangchen Liu, Jongmin Lee, Pieter Abbeel
    CoRL 2024
    paper code website
  • [C24] Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies
    Haanvid Lee, Tri Wahyu Guntara, Jongmin Lee, Yung-Kyun Noh, Kee-Eung Kim
    ICLR 2024
    paper spotlight

2023

  • [C21] AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction Estimation
    Daiki E. Matsunaga*, Jongmin Lee*, Jaeseok Yoon, Stefanos Leonardos, Pieter Abbeel, Kee-Eung Kim
    NeurIPS 2023
    paper code
  • [C20] SafeDICE: Offline Safe Imitation Learning with Non-Preferred Demonstrations
    Youngsoo Jang, Geon-Hyeong Kim, Jongmin Lee, Sungryull Sohn, Byoungjip Kim, Honglak Lee, Moontae Lee
    NeurIPS 2023
    paper code
  • [C19] Tempo Adaptation in Non-stationary Reinforcement Learning
    Hyunin Lee, Yuhao Ding, Jongmin Lee, Ming Jin, Javad Lavaei, Somayeh Sojoudi
    NeurIPS 2023
    paper

2022

  • [C15] LobsDICE: Offline Imitation Learning from Observation via Stationary Distribution Correction Estimation
    Geon-Hyeong Kim*, Jongmin Lee*, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim
    NeurIPS 2022
    paper code
  • [C14] Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions
    Haanvid Lee, Jongmin Lee, Yunseon Choi, Wonseok Jeon, Byung-Jun Lee, Yung-Kyun Noh, Kee-Eung Kim
    NeurIPS 2022
    paper
  • [C18] COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
    Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez
    ICLR 2022
    paper code spotlight
  • [C17] DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations
    Geon-Hyeong Kim, Seokin Seo, Jongmin Lee, Wonseok Jeon, HyeongJoo Hwang, Hongseok Yang, Kee-Eung Kim
    ICLR 2022
    paper code
  • [C16] GPT-Critic: Offline Reinforcement Learning for End-to-End Task-Oriented Dialogue Systems
    Youngsoo Jang, Jongmin Lee, Kee-Eung Kim
    ICLR 2022
    paper code

2021

  • [C12,W5] OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation
    Jongmin Lee*, Wonseok Jeon*, Byung-Jun Lee, Joelle Pineau, Kee-Eung Kim
    ICML 2021
    ICLR Workshop on Never-Ending RL 2021
    paper code
  • [C11] Representation Balancing Offline Model-based Reinforcement Learning
    Byung-Jun Lee, Jongmin Lee, Kee-Eung Kim
    ICLR 2021
    paper code
  • [C13] Monte-Carlo Planning and Learning with Language Action Value Estimates
    Youngsoo Jang, Seokin Seo, Jongmin Lee, Kee-Eung Kim
    ICLR 2021
    paper code

2020

  • [C7] Reinforcement Learning for Control with Multiple Frequencies
    Jongmin Lee, Byung-Jun Lee, Kee-Eung Kim
    NeurIPS 2020
    paper code
  • [C10] Batch Reinforcement Learning with Hyperparameter Gradients
    Byung-Jun Lee*, Jongmin Lee*, Peter Vrancx, Dongho Kim, Kee-Eung Kim
    ICML 2020
    paper code
  • [C8] Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients
    Jongmin Lee, Wonseok Jeon, Geon-Hyeong Kim, Kee-Eung Kim
    AAAI 2020
    paper
  • [C9,W4] Bayes-Adaptive Monte-Carlo Planning and Learning for Goal-Oriented Dialogues
    Youngsoo Jang, Jongmin Lee, Kee-Eung Kim
    AAAI 2020
    NeurIPS Workshop on Conversational AI 2019
    paper

2019

  • [C5] Trust Region Sequential Variational Inference
    Geon-Hyeong Kim, Youngsoo Jang, Jongmin Lee, Wonseok Jeon, Hongseok Yang, Kee-Eung Kim
    ACML 2019
    paper
  • [C6] PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules
    Youngsoo Jang*, Jongmin Lee*, Jaeyoung Park*, Kyeng-Hun Lee, Pierre Lison, Kee-Eung Kim
    EMNLP 2019
    paper code

2018

  • [C4] Monte-Carlo Tree Search for Constrained POMDPs
    Jongmin Lee, Geon-Hyeong Kim, Pascal Poupart, Kee-Eung Kim
    NeurIPS 2018
    paper code
  • [W3] Monte-Carlo Tree Search for Constrained MDPs
    Jongmin Lee, Geon-Hyeong Kim, Pascal Poupart, Kee-Eung Kim
    ICML Workshop on Planning and Learning (PAL-18), 2018
    paper
  • [J1] Layered Behavior Modeling via Combining Descriptive and Prescriptive Approaches: a Case Study of Infantry Company Engagement
    Jang Won Bae, Junseok Lee, Do-Hyung Kim, Kanghoon Lee, Jongmin Lee, Kee-Eung Kim, Il-Chul Moon
    IEEE Transactions on System, Man, and Cybernetics: Systems 2018
    paper

2017

  • [C3,W2] Constrained Bayesian Reinforcement Learning via Approximate Linear Programming
    Jongmin Lee, Youngsoo Jang, Pascal Poupart, Kee-Eung Kim
    IJCAI 2017
    Scaling-Up Reinforcement Learning Workshop at ECML PKDD (SURL), 2017
    paper
  • [C2] Hierarchically-partitioned Gaussian Process Approximation
    Byung-Jun Lee, Jongmin Lee, Kee-Eung Kim
    AISTATS 2017
    paper

2016

  • [W1] Multi-View Automatic Lip-Reading using Neural Network
    Daehyun Lee, Jongmin Lee, Kee-Eung Kim
    ACCV Workshop on Multi-view Lip-reading/Audio-visual Challenges, 2016
    paper
  • [C1] Bayesian Reinforcement Learning with Behavioral Feedback
    Teakgyu Hong, Jongmin Lee, Kee-Eung Kim, Pedro A. Ortega, Daniel Lee
    IJCAI 2016
    paper