Learning Intelligent Machine Lab

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Learning Intelligent Machine Lab

Our research interest is focused on Artificial Intelligence (AI) and its applications to industrial and scientific problems. Specifically, we pursue principled approaches to incomplete data problems in machine learning via the view of statistics and mathematics. These topics are profoundly related to statistical learning, meta learning, and causal reasoning. Currently, we are working with the following research topics:
- Causal Learning
- Stochastic Optimization
- Planning / Reinforcement Learning
- Automated Machine Learning

Learning Intelligent Machine Lab 에서는 수학과 통계학적 원리를 바탕으로 인공지능 연구를 진행하고 있으며, 주로 아래 분야의 주제에 집중하고 있습니다.
- 인과학습(Causal Learning)
- 확률적최적화(Stochastic Optimization)
- 플래닝/강화학습(Planning / Reinforcement Learning)
- 자동화된 기계학습(Automated Machine Learning)


Artificial Intelligence, Causal Learning, Stochastic Optimization, Optimal Control


Large-scale Causal Reasoning, Machine Reasoning

Research Keywords and Topics

- Artificial Intelligence, Causal Learning, Stochastic Optimization, Optimal Control
- 인공지능, 인과학습, 확률적 최적화, 최적 제어

Research Publications

- Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR oral), Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density Network, Sungjoon Choi / Sanghoon Hong / Kyungjae Lee / Sungbin Lim, (2020)
- Advances in Neural Information Processing Systems (NeurIPS), Fast AutoAugment, Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, Sungwoong Kim, (2019)
- Annals of Probability, A Sobolev space theory for stochastic partial differential equations with time-fractional derivatives, Ildoo Kim, Kyeong-Hun Kim, Sungbin Lim, (2019)


  • EE. 정보/통신
  • EE01. 정보이론
  • EE0108. 인공지능


  • 정보-지식-지능화 사회 구현
  • 012300. 인공지능/지능로봇 기술


  • 녹색기술관련 과제 아님
  • 녹색기술관련 과제 아님
  • 999. 녹색기술 관련과제 아님


  • IT 분야
  • 정보처리 시스템 및 S/W
  • 010314. 신호처리기술(영상/음성처리/인식/합성)