Geon-Hyeong Kim (김건형)
Geon-Hyeong Kim (김건형)
Research Interest: Imitation Learning, Reinforcement Learning, Large Language Model, Machine Learning
Email: geonhyeong.kim01 [at] gmail.com / ghkim [at] lgresearch.ai
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Experience
Experience
2022. 07. - Current. AI Scientist at Advanced ML Lab, LG AI Research
Education
Education
2015. 03. - 2022. 08. PhD, School of Computing, KAIST, Korea (Advisor: Kee-Eung Kim, Hongseok Yang)
2013. 03. - 2015. 02. MS, Mathematical Sciences, KAIST, Korea (Advisor: Chang-Ock Lee)
2009. 02. - 2013. 02. BS, Mathematical Sciences, KAIST, Korea (Minor in School of Computing)
Publications
Publications
International
International
[P1] SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety
Geon-Hyeong Kim, Youngsoo Jang, Yu Jin Kim, Byoungjip Kim, Honglak Lee, Kyunghoon Bae, and Moontae Lee
Preprint 2025
[C13] Online Pre-Training for Offline-to-Online Reinforcement Learning
Yongjae Shin, Jeonghye Kim, Whiyoung Jung, Sunghoon Hong, Deunsol Yoon, Youngsoo Jang, Geon-Hyeong Kim, Jongseong Chae, Youngchul Sung, Kanghoon Lee, and Woohyung Lim
ICML 2025
[C12, W2] Prospector: Improving LLM Agents with Self-Asking and Trajectory Ranking
Byoungjip Kim, Youngsoo Jang, Lajanugen Logeswaran, Geon-Hyeong Kim, Yu Jin Kim, Honglak Lee, and Moontae Lee
EMNLP Finding, 2024
NeurIPS Workshop on Foundation Models for Decision Making, 2023
[C11, W3] Degeneration-free Policy Optimization: RL Fine-Tuning for Language Models without Degeneration
Youngsoo Jang, Geon-Hyeong Kim, Byoungjip Kim, Yu Jin Kim, Honglak Lee, and Moontae Lee
ICML 2024
ICLR Workshop on Generative Models for Decision Making. 2024
[C10] SafeDICE: Offline Safe Imitation Learning with Non-Preferred Demonstrations
Youngsoo Jang, Geon-Hyeong Kim, Jongmin Lee, Sungryull Sohn, Byoungjip Kim, Honglak Lee, and Moontae Lee
NeurIPS 2023
[C9] Information-Theoretic State Space Model for Multi-View Reinforcement Learning
HyeongJoo Hwang, Seokin Seo, Youngsoo Jang, Sungyoon Kim, Geon-Hyeong Kim, Seunghoon Hong, and Kee-Eung Kim
ICML 2023
[C8] LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation
Geon-Hyeong Kim*, Jongmin Lee*, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim (*: equal contribution)
NeurIPS 2022
[C7] 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
[C6] Multi-View Representation Learning via Total Correlation Objective
HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim
NeurIPS 2021
[C5] Variational Interaction Information Maximization for Cross-domain Disentanglement
HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim
NeurIPS 2020
[C4] Variational Inference for Sequential Data with Future Likelihood Estimates
Geon-Hyeong Kim, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim
ICML 2020
[C3] Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients
Jongmin Lee, Wonseok Jeon, Geon-Hyeong Kim, Kee-Eung Kim
AAAI 2020
[C2] Trust Region Sequential Variational Inference
Geon-Hyeong Kim, Youngsoo Jang, Jongmin Lee, Wonseok Jeon, Hongseok Yang, Kee-Eung Kim
ACML 2019
[C1] Monte-Carlo Tree Search for Constrained POMDPs
Jongmin Lee, Geon-Hyeong Kim, Pascal Poupart, Kee-Eung Kim
NeurIPS 2018
[W1] 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
[J1] Bayesian optimistic Kullback–Leibler exploration
Kanghoon Lee, Geon-Hyeong Kim, Pedro Ortega, Daniel D. Lee, Kee-Eung Kim
Machine Learning, Springer, 2018
Academic Talks
Academic Talks
LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation
LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation
2022. 11. 28 - 12.02, NeurIPS (virtual)
DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations
DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations
2022. 04. 25 - 29, ICLR (virtual)
Variational Inference for Sequential Data with Future Likelihood Estimates
Variational Inference for Sequential Data with Future Likelihood Estimates
2020. 07. 16 - 17, ICML (virtual)
2019. 09. 27, Kakao, Korea
Trust Region Sequential Variational Inference
Trust Region Sequential Variational Inference
2019. 11. 19, ACML
Reviewer
Reviewer
ICML (2021 - 2024)
NeurIPS (2020 - 2024)
ICLR (2021 - 2024)
ACML (2019, 2020)
IJCAI (2021, 2022)
Teaching Experience
Teaching Experience
KAIST-Samsung AI Expert Program: Introduction to Reinforcement Learning, TA at KAIST (2020)
Data Structures (CS206), TA at KAIST (2016 Fall, 2018 Spring)
Introduction to Artificial Intelligence (CS470), TA at KAIST (2017 Fall)
Introduction to Linear Algebra (MAS109), TA at KAIST (2013 Fall, 2014 Spring, 2014 Fall)
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