Geon-Hyeong Kim (김건형)

Email: ghkim@ai.kaist.ac.kr

Research Interest: Imitation Learning, Reinforcement Learning, Variational Inference, Machine Learning

Education

2015. 03. - Current: PhD Candidate, School of Computing, KAIST, Korea (Advisor: Kee-Eung Kim, Co-Advisor: 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

International Conference

  • HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong and Kee-Eung Kim, "Variational Interaction Information Maximization for Cross-domain Disentanglement", NeurIPS, 2020

  • Geon-Hyeong Kim, Youngsoo Jang, Hongseok Yang and Kee-Eung Kim, "Variational Inference for Sequential Data with Future Likelihood Estimates", ICML, 2020

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

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

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

International Journal

  • Kanghoon Lee, Geon-Hyeong Kim, Pedro Ortega, Daniel D Lee and Kee-Eung Kim, "Bayesian optimistic Kullback–Leibler exploration ", Machine Learning, Springer, 2018

International Workshop

  • 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

Academic Talks

Variational Inference for Sequential Data with Future Likelihood Estimates

  • 2020. 07. 16 - 17. ICML

  • 2019. 09. 27. Kakao, Korea

Trust Region Sequential Variational Inference

  • 2019. 11. 19. ACML

Reviewer

  • ACML (2019)

  • ICLR (2020, 2021)

  • ICML (2021)

  • IJCAI (2021)

  • NeurIPS (2020, 2021)

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)