Members

Member

HeeSun BAE

PhD Students

Applied Artificial Intelligence Laboratory Industrial and Systems Engineering, KAIST 291 Daehak-ro, Yuseong-gu, Daejeon 305-338, Republic of Korea

KAIST E2-2, 4109

cat2507@kaist.ac.kr

Research Interest

  • Machine learning
  • Representation Learning
  • Distribution Shift
  • Generalization Problem: Noisy label, Spurious Correlation

Education

    Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea (Mar. 2022 ~ )

  • Doctoral Degree Program in Industrial and Systems Engineering, AAILab
  • Academic Advisor: Professor Il-Chul Moon

    Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea (Mar. 2020 - Feb. 2022)

  • Master's Degree Program in Industrial and Systems Engineering, AAILab
  • Academic Advisor: Professor Il-Chul Moon

    Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea (Mar. 2015 - Feb. 2020)

  • Bachelor of Science in Industrial and Systems Engineering

Publication

    International Conference

  • HeeSun Bae, Seungjae Shin, Byeonghu Na, Il-Chul Moon, Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning, International Conference on Learning Representations (ICLR 2024), Vienna, Austria, May 7-11, 2024
  • Seungjae Shin, Heesun Bae, Byeonghu Na, Yoon-Yeong Kim, Il-Chul Moon, Unknown Domain Inconsistency Minimization for Domain Generalization, International Conference on Learning Representations (ICLR 2024), Vienna, Austria, May 7-11, 2024 
  • Byeonghu Na, Yeongmin Kim, HeeSun Bae, Jung Hyun Lee, Se Jung Kwon, Wanmo Kang, Il-Chul Moon, Label-Noise Robust Diffusion Models, International Conference on Learning Representations (ICLR 2024), Vienna, Austria, May 7-11, 2024
  • Youngjae Cho, HeeSun Bae, Seungjae Shin, YeoDong Youn, Weonyoung Joo, and Il-Chul Moon, Make Prompts Adaptable : Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior , AAAI Conference on Artificial Intelligence (AAAI 2024), Vancouver, Feb. 22-25
  • Seungjae Shin*, HeeSun Bae*, DongHyeok Shin, Weonyoung Joo, Il-Chul Moon, Loss Curvature Matching for Dataset Selection and Condensation, International Conference on Artificial Intelligence and Statistics (AISTATS) 2023
  • Seungjae Shin*, Heesun Bae*, Giwoon Kim, Youngsoon Cho, Dongwook Lee, Donggil Jeong, HyunJoon Kim, Hyunjung Lee, Hyungjun Moon, Evaluation of Optimal Scene Time Interval for Out-of-hospital Cardiac Arrest using a Deep Neural Network, American Journal of Emergency Medicine, Volume 63, January 2023, Pages 29-37
  • HeeSun Bae*, Seungjae Shin*, Byeonghu Na, JoonHo Jang, Kyungwoo Song, Il-Chul Moon, From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model, International Conference on Machine Learning (ICML 2022), Baltimore, Jul 17, 2022
  • Seungjae Shin, Byeonghu Na, HeeSun Bae, JoonHo Jang, Hyemi Kim, Kyungwoo Song, Youngjae Cho, and Il-Chul Moon. 2022. Improving Group-based Robustness and Calibration via Ordered Risk and Confidence Regularization. In The Workshop on Spurious Correlations, Invariance, and Stability, International Conference on Machine Learning (SCIS at ICML 2022), July 22, 2022, Baltimore, Maryland USA. 

    Domestic Conference

  • 배희선, 신승재, 문일철, 배장원 (2021) "스마트 그리드 기반 에너지 시스템 운영을 위한 배전계통 조류계산 시뮬레이션 모델 개발" 한국시뮬레이션학회

Award

  • IE Frontier 우수상, KAIST, 2017
  • Winner, Qualcomm Innovation Fellowship Korea, 2022
  • Winner, Qualcomm Innovation Fellowship Korea, 2023