Multi-view Automatic Lip-Reading Using Neural Network. - Daehyun Lee; Jongmin Lee 004; Kee-Eung Kim (ACCV Workshops (2), 2016)

Bayesian Reinforcement Learning with Behavioral Feedback. - Teakgyu Hong; Jongmin Lee 004; Kee-Eung Kim; Pedro A. Ortega; Daniel D. Lee (IJCAI, 2016)

Hierarchically-partitioned Gaussian Process Approximation. - Byung-Jun Lee 001; Jongmin Lee 004; Kee-Eung Kim (AISTATS, 2017)

Constrained Bayesian Reinforcement Learning via Approximate Linear Programming. - Jongmin Lee 004; Youngsoo Jang; Pascal Poupart; Kee-Eung Kim (IJCAI, 2017)

Monte-Carlo Tree Search for Constrained POMDPs. - Jongmin Lee 004; Geon-hyeong Kim; Pascal Poupart; Kee-Eung Kim (NeurIPS, 2018)

Trust Region Sequential Variational Inference. - Geon-hyeong Kim; Youngsoo Jang; Jongmin Lee 004; Wonseok Jeon; Hongseok Yang; Kee-Eung Kim (ACML, 2019)

PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules. - Youngsoo Jang; Jongmin Lee 004; Jaeyoung Park; Kyeng-Hun Lee; Pierre Lison; Kee-Eung Kim (EMNLP/IJCNLP (3), 2019)

Bayes-Adaptive Monte-Carlo Planning and Learning for Goal-Oriented Dialogues. - Youngsoo Jang; Jongmin Lee 004; Kee-Eung Kim (AAAI, 2020)

Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients. - Jongmin Lee 004; Wonseok Jeon; Geon-Hyeong Kim; Kee-Eung Kim (AAAI, 2020)

Batch Reinforcement Learning with Hyperparameter Gradients. - Byung-Jun Lee 001; Jongmin Lee 004; Peter Vrancx; Dongho Kim; Kee-Eung Kim (ICML, 2020)

Reinforcement Learning for Control with Multiple Frequencies. - Jongmin Lee 004; Byung-Jun Lee 001; Kee-Eung Kim (NeurIPS, 2020)

Layered Behavior Modeling via Combining Descriptive and Prescriptive Approaches: A Case Study of Infantry Company Engagement. - Jang Won Bae; Junseok Lee 001; Do-Hyung Kim; Kanghoon Lee; Jongmin Lee 004; Kee-Eung Kim; Il-Chul Moon (IEEE Trans. Syst. Man Cybern. Syst., 2020)

Representation Balancing Offline Model-based Reinforcement Learning. - Byung-Jun Lee 001; Jongmin Lee 004; Kee-Eung Kim (ICLR, 2021)

Monte-Carlo Planning and Learning with Language Action Value Estimates. - Youngsoo Jang; Seokin Seo; Jongmin Lee 004; Kee-Eung Kim (ICLR, 2021)

OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation. - Jongmin Lee 004; Wonseok Jeon; Byung-Jun Lee 001; Joelle Pineau; Kee-Eung Kim (ICML, 2021)

COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation. - Jongmin Lee 004; Cosmin Paduraru; Daniel J. Mankowitz; Nicolas Heess; Doina Precup; Kee-Eung Kim; Arthur Guez (ICLR, 2022)

GPT-Critic: Offline Reinforcement Learning for End-to-End Task-Oriented Dialogue Systems. - Youngsoo Jang; Jongmin Lee 004; Kee-Eung Kim (ICLR, 2022)

DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations. - Geon-Hyeong Kim; Seokin Seo; Jongmin Lee 004; Wonseok Jeon; HyeongJoo Hwang; Hongseok Yang; Kee-Eung Kim (ICLR, 2022)

LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation. - Geon-Hyeong Kim; Jongmin Lee 004; Youngsoo Jang; Hongseok Yang; Kee-Eung Kim (NeurIPS, 2022)

Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions. - Haanvid Lee; Jongmin Lee 004; Yunseon Choi; Wonseok Jeon; Byung-Jun Lee 001; Yung-Kyun Noh; Kee-Eung Kim (NeurIPS, 2022)

LobsDICE: Offline Imitation Learning from Observation via Stationary Distribution Correction Estimation. - Geon-Hyeong Kim; Jongmin Lee 004; Youngsoo Jang; Hongseok Yang; Kee-Eung Kim (CoRR, 2022)

SafeDICE: Offline Safe Imitation Learning with Non-Preferred Demonstrations. - Youngsoo Jang; Geon-Hyeong Kim; Jongmin Lee 004; Sungryull Sohn; Byoungjip Kim; Honglak Lee; Moontae Lee (NeurIPS, 2023)

AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction Estimation. - Daiki E. Matsunaga; Jongmin Lee 004; Jaeseok Yoon; Stefanos Leonardos; Pieter Abbeel; Kee-Eung Kim (NeurIPS, 2023)

Body Transformer: Leveraging Robot Embodiment for Policy Learning. - Carmelo Sferrazza; Dun-Ming Huang; Fangchen Liu; Jongmin Lee 004; Pieter Abbeel (CoRL, 2024)

Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies. - Haanvid Lee; Tri Wahyu Guntara; Jongmin Lee 004; Yung-Kyun Noh; Kee-Eung Kim (ICLR, 2024)

Mitigating Covariate Shift in Behavioral Cloning via Robust Stationary Distribution Correction. - Seokin Seo; Byung-Jun Lee 001; Jongmin Lee 004; HyeongJoo Hwang; Hongseok Yang; Kee-Eung Kim (NeurIPS, 2024)

SEMDICE: Off-policy State Entropy Maximization via Stationary Distribution Correction Estimation. - Jongmin Lee 004; Meiqi Sun; Pieter Abbeel (ICLR, 2025)