Jongmin Lee

Jongmin Lee (이종민)

E-mail: jongmin.lee012 [at] gmail.com

Education

2017. 03. - Current: PhD Candidate, 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

Apr 2021 - Aug 2021: Research Scientist Intern at DeepMind (Host: Arthur Guez)

Publications

International

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

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

  • Youngsoo Jang, Jongmin Lee, Kee-Eung Kim, Offline Reinforcement Learning for End-to-End Task-Oriented Dialogue Systems, ICLR, 2022 (to appear)

  • Jongmin Lee*, Wonseok Jeon*, Byung-Jun Lee, Joelle Pineau, Kee-Eung Kim, "OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation", ICML, 2021 (*: equal contribution)
    (also presented at Never-Ending RL Workshop at ICLR 2021)

  • Byung-Jun Lee, Jongmin Lee, Kee-Eung Kim, "Representation Balancing Offline Model-based Reinforcement Learning", ICLR, 2021

  • Youngsoo Jang, Seokin Seo, Jongmin Lee, Kee-Eung Kim, "Monte-Carlo Planning and Learning with Language Action Value Estimates", ICLR, 2021

  • Jongmin Lee, Byung-Jun Lee, Kee-Eung Kim, "Reinforcement Learning for Control with Multiple Frequencies", NeurIPS, 2020

  • Byung-Jun Lee*, Jongmin Lee*, Peter Vrancx, Dongho Kim, Kee-Eung Kim, "Batch Reinforcement Learning with Hyperparameter Gradients", ICML, 2020 (*: equal contribution)

  • Jongmin Lee, Wonseok Jeon, Geon-Hyeong Kim, Kee-Eung Kim, "Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients", AAAI, 2020

  • Youngsoo Jang, Jongmin Lee, Kee-Eung Kim, "Bayes-Adaptive Monte-Carlo Planning and Learning for Goal-Oriented Dialogues", AAAI, 2020
    (its previous version was presented at 3rd Conversational AI Workshop at NeurIPS 2019)

  • Geon-Hyeong Kim, Youngsoo Jang, Jongmin Lee, Wonseok Jeon, Hongseok Yang, and Kee-Eung Kim, "Trust Region Sequential Variational Inference", ACML, 2019

  • Youngsoo Jang*, Jongmin Lee*, Jaeyoung Park*, Kyeng-Hun Lee, Pierre Lison, and Kee-Eung Kim, "PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules", EMNLP System Demonstrations, 2019 (*: equal contribution)

  • Jongmin Lee, Geon-Hyeong Kim, Pascal Poupart, and Kee-Eung Kim, "Monte-Carlo Tree Search for Constrained POMDPs", NeurIPS, 2018

  • Jongmin Lee, Geon-Hyeong Kim, Pascal Poupart, and Kee-Eung Kim, "Monte-Carlo Tree Search for Constrained MDPs", ICML Workshop on Planning and Learning (PAL-18) , 2018

  • Jang Won Bae, Junseok Lee, Do-Hyung Kim, Kanghoon Lee, Jongmin Lee, Kee-Eung Kim and Il-Chul Moon, "Layered Behavior Modeling via Combining Descriptive and Prescriptive Approaches: a Case Study of Infantry Company Engagement ", IEEE Transactions on System, Man, and Cybernetics: Systems, 2018

  • Jongmin Lee, Youngsoo Jang, Pascal Poupart, and Kee-Eung Kim, "Constrained Bayesian Reinforcement Learning via Approximate Linear Programming", IJCAI, 2017
    (its shortened version was presented at Scaling-Up Reinforcement Learning Workshop at ECML PKDD (SURL), 2017)

  • Byung-Jun Lee, Jongmin Lee, and Kee-Eung Kim, "Hierarchically-partitioned Gaussian Process Approximation", AISTATS, 2017

  • Daehyun Lee, Jongmin Lee, and Kee-Eung Kim, "Multi-View Automatic Lip-Reading using Neural Network", ACCV Workshop on Multi-view Lip-reading/Audio-visual Challenges, 2016

  • Teakgyu Hong, Jongmin Lee, Kee-Eung Kim, Pedro A. Ortega, and Daniel Lee, "Bayesian Reinforcement Learning with Behavioral Feedback", IJCAI, 2016

Domestic

  • Geon-Hyeong Kim, Youngsoo Jang, Jongmin Lee, and Kee-Eung Kim, "A Study on Efficient Multi-Task Offline Model-based Reinforcement Learning", 한국소프트웨어종합학술대회, 2021

  • Byung-Jun Lee, Jongmin Lee, Yunseon Choi, Youngsoo Jang, and Kee-Eung Kim, "A Study on Application of Efficient Lifelong Learning Algorithm to Model-based Reinforcement Learning", 한국소프트웨어종합학술대회, 2020

  • Jongmin Lee, Geon-Hyeong Kim, and Kee-Eung Kim, "A Study on Monte-Carlo Tree Search in Continuous Action Spaces", 한국통신학회 하계종합학술발표회 논문집, 2019

  • Jongmin Lee, Jungpyo Hong, Jaeyoung Park, Kanghoon Lee, Kee-Eung Kim, Il-Chul Moon, and Jae-Hyun Park, "Case Studies on Planning and Learning for Large-Scale CGFs with POMDPs through Counterfire and Mechanized Infantry Scenarios", KIISE Transactions on Computing Practices, 2017

  • Jungpyo Hong, Jongmin Lee, Kanghoon Lee, Sanggyu Han, Kee-Eung Kim, Il-Chul Moon, and Jae-Hyeon Park, "A Case Study on Planning and Learning for Large-Scale CGFs with POMDPs", 한국정보과학회 학술발표논문집, 2016

  • Joon Shik Kim, Jongmin Lee, Chung-Yeon Lee, Beom-Jin Lee, Byoung-Tak Zhang, "Prediction of Cognitive Tasks via Hypernetwork Sampling of EEG Data", 한국정보과학회 학술발표논문집, 2012

Awards and Honors

  • 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)

  • ICML (2019, 2020,2021)

  • AAAI (2020,2021)

  • ICLR (2020,2021)

  • IJCAI (2021)

  • ACML (2017, 2019, 2021)

  • Machine Learning Journal (2017, 2019)

  • Journal of Artificial Intelligence Research (2019)

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

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