Jongmin Lee

I'm a postdoc at UC Berkeley, advised by Pieter Abbeel. I received my PhD from KAIST, where I was fortunate to be advised by Kee-Eung Kim.

  • E-mail: jongmin.lee012 [at] gmail dot com / jongmin.lee [at] berkeley dot edu

[Google Scholar]

Education

2017. 03. - 2022. 02: PhD, School of Computing, KAIST, Korea (Advisor: Kee-Eung Kim)

  • Thesis: Algorithms for Safe Reinforcement Learning

2015. 03. - 2017. 02.: MS, School of Computing, KAIST, Korea (Advisor: Kee-Eung Kim)

  • Thesis: Constrained Bayesian Reinforcement Learning via Approximate Linear Programming

2009. 03. - 2014. 02.: BS, Department of Computer Science and Engineering, Seoul National University, Korea

Experience

Publications

International

[C18] LobsDICE: Offline Imitation Learning from Observation via Stationary Distribution Correction Estimation [paper]

  • Geon-Hyeong Kim*, Jongmin Lee*, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim (*: equal contribution)

  • NeurIPS 2022 (to appear)

[C17] 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 (to appear)

[C16] COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation [paper] [code]

  • Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez

  • ICLR 2022 (spotlight)

[C15] DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations [paper] [code]

  • Geon-Hyeong Kim, Seokin Seo, Jongmin Lee, Wonseok Jeon, HyeongJoo Hwang, Hongseok Yang, Kee-Eung Kim

  • ICLR 2022

[C14] GPT-Critic: Offline Reinforcement Learning for End-to-End Task-Oriented Dialogue Systems [paper]

  • Youngsoo Jang, Jongmin Lee, Kee-Eung Kim

  • ICLR 2022

[C13,W4] OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation [paper] [code]

[C12] Representation Balancing Offline Model-based Reinforcement Learning [paper] [code]

  • Byung-Jun Lee, Jongmin Lee, Kee-Eung Kim

  • ICLR 2021

[C11] Monte-Carlo Planning and Learning with Language Action Value Estimates [paper] [code]

  • Youngsoo Jang, Seokin Seo, Jongmin Lee, Kee-Eung Kim

  • ICLR 2021

[C10] Reinforcement Learning for Control with Multiple Frequencies [paper] [code]

[C9] Batch Reinforcement Learning with Hyperparameter Gradients [paper] [code]

  • Byung-Jun Lee*, Jongmin Lee*, Peter Vrancx, Dongho Kim, Kee-Eung Kim (*: equal contribution)

  • ICML 2020

[C8] Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients [paper]

  • Jongmin Lee, Wonseok Jeon, Geon-Hyeong Kim, Kee-Eung Kim

  • AAAI 2020

[C7,W4] Bayes-Adaptive Monte-Carlo Planning and Learning for Goal-Oriented Dialogues [paper]

[C6] Trust Region Sequential Variational Inference [paper]

  • Geon-Hyeong Kim, Youngsoo Jang, Jongmin Lee, Wonseok Jeon, Hongseok Yang, and Kee-Eung Kim

  • ACML 2019

[C5] PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules [paper] [code]

[C4] Monte-Carlo Tree Search for Constrained POMDPs [paper] [code]

  • Jongmin Lee, Geon-Hyeong Kim, Pascal Poupart, and Kee-Eung Kim

  • NeurIPS 2018

[W3] Monte-Carlo Tree Search for Constrained MDPs [paper]

[J1] Layered Behavior Modeling via Combining Descriptive and Prescriptive Approaches: a Case Study of Infantry Company Engagement [paper]

  • Jang Won Bae, Junseok Lee, Do-Hyung Kim, Kanghoon Lee, Jongmin Lee, Kee-Eung Kim and Il-Chul Moon

  • IEEE Transactions on System, Man, and Cybernetics: Systems, 2018

[C3,W2] Constrained Bayesian Reinforcement Learning via Approximate Linear Programming [paper]

[C2] Hierarchically-partitioned Gaussian Process Approximation [paper]

[W1] Multi-View Automatic Lip-Reading using Neural Network [paper]

  • Daehyun Lee, Jongmin Lee, and Kee-Eung Kim

  • ACCV Workshop on Multi-view Lip-reading/Audio-visual Challenges, 2016

[C1] Bayesian Reinforcement Learning with Behavioral Feedback [paper]

  • Teakgyu Hong, Jongmin Lee, Kee-Eung Kim, Pedro A. Ortega, and Daniel Lee

  • IJCAI 2016

Domestic

A Study on Efficient Multi-Task Offline Model-based Reinforcement Learning

  • Geon-Hyeong Kim, Youngsoo Jang, Jongmin Lee, and Kee-Eung Kim

  • 한국소프트웨어종합학술대회, 2021

A Study on Application of Efficient Lifelong Learning Algorithm to Model-based Reinforcement Learning

  • Byung-Jun Lee, Jongmin Lee, Yunseon Choi, Youngsoo Jang, and Kee-Eung Kim

  • 한국소프트웨어종합학술대회, 2020

A Study on Monte-Carlo Tree Search in Continuous Action Spaces

  • Jongmin Lee, Geon-Hyeong Kim, and Kee-Eung Kim

  • 한국통신학회 하계종합학술발표회 논문집, 2019

Case Studies on Planning and Learning for Large-Scale CGFs with POMDPs through Counterfire and Mechanized Infantry Scenarios

  • Jongmin Lee, Jungpyo Hong, Jaeyoung Park, Kanghoon Lee, Kee-Eung Kim, Il-Chul Moon, and Jae-Hyun Park

  • KIISE Transactions on Computing Practices, 2017

A Case Study on Planning and Learning for Large-Scale CGFs with POMDPs

  • Jungpyo Hong, Jongmin Lee, Kanghoon Lee, Sanggyu Han, Kee-Eung Kim, Il-Chul Moon, and Jae-Hyeon Park

  • 한국정보과학회 학술발표논문집, 2016

Awards and Honors

  • Outstanding Ph.D. Thesis Award, School of Computing at KAIST, 2022

  • Qualcomm-KAIST Innovation Awards - Paper Competition, Qualcomm, 2019

  • Society of Global Ph.D. Fellows Outstanding Presentation Award, 5th SGPF Annual Conference, 2018

  • Global Ph.D. Fellowship, National Research Foundation of Korea, 2018 ~ 2020

  • Naver Ph.D. Fellowship, NAVER, 2017

Reviewer

  • NeurIPS (2016, 2018, 2019, 2020, 2021, 2022)

  • ICML (2019, 2020,2021, 2022)

  • AAAI (2020,2021, 2022)

  • ICLR (2020,2021, 2022)

  • IJCAI (2021, 2022)

  • ACML (2017, 2019, 2021)

  • Machine Learning Journal (2017, 2019)

  • Journal of Artificial Intelligence Research (2019)

  • Transactions on Machine Learning Research (2022)

Teaching Experiences

  • KAIST-Samsung AI Expert Program: Introduction to Reinforcement Learning & Deep Reinforcement Learning TA, KAIST, 2020

  • KAIST-Samsung AI Expert Program: Introduction to Tensorflow & Reinforcement Learning, TA, KAIST, 2019

  • Data Structure (CS206), TA, KAIST, 2019

  • Introduction to Programming (CS101), Head TA, KAIST, 2018

  • Artificial Intelligence and Machine Learning (CS570), TA, KAIST, Spring 2016

  • Artificial Intelligence and Machine Learning, TA, KMOOC, Fall 2015

  • Peer group seminar: Agile web development for non-majors (009.032), Seoul National University, Fall 2012

Extracurricular Activities

  • Student Representative of School of Computing, KAIST, 2017. 1 ~ 2017. 12

  • Vice Student Representative of School of Computing, KAIST, 2015. 3 ~ 2016. 2