About Me
I’m a researcher in the LLM team @ SAIT, Samsung Electronics.
My research interest includes:
- Post-hoc analysis & adaptation of Foundation models (e.g., Large-Language Models and Vision-Language Models).
- Explainability and interpretability of Foundation models.
- Generalization of Foundation models via the lens of loss landscape.
Education
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Ph.D. in Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST)
Mar. 2019 - Feb. 2024 -
M.S. in Industrial Engineering (Mathematical optimization), Seoul National University (SNU)
Mar. 2017 - Feb. 2019 -
B.S. in Industrial Engineering & Mathematical Science, Seoul National University (SNU)
Mar. 2013 - Feb. 2017
Published Papers
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A Simple Remedy for Dataset Bias via Self-Influence: A Mislabeled Sample Perspective
Yeonsung Jung*, Jaeyun Song*, June Yong Yang*, Jin-Hwa Kim, Sung-Yub Kim, and Eunho Yang (*: equal contribution)
NeurIPS, 2024 -
TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias
Sanghyun Jo*, Soohyun Ryu*, Sungyub Kim, Eunho Yang, Kyungsu Kim (*: equal contribution)
European Conference on Computer Vision (ECCV), 2024 -
GEX: A flexible method for approximating influence via Geometric Ensemble
Sung-Yub Kim, Kyungsu Kim, Eunho Yang
NeurIPS, 2023 -
RGE: A Repulsive Graph Rectification for Node Classification via Influence
Jaeyun Song*, Sung-Yub Kim*, Eunho Yang (*: equal contribution)
ICML, 2023 -
Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel
Sung-Yub Kim, Sihwan Park, Kyungsu Kim, Eunho Yang
ICLR, 2023 (Spotlighted) -
Generalized Tsallis Entropy Reinforcement Learning and Its Application to Soft Mobile Robots
Kyungjae Lee, Sung-Yub Kim, Sungbin Lim, Sungjoon Choi, Mineui Hong, Jaein Kim, Yong-Lae Park, and Songhwai Oh
Robotics: Science and Systems Foundation
Projects
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Autonomous Intelligent Digital Companion Framework and Application
Korea Electronics Technology Institute, 2019-2021 -
A study on adaptive point and regional OOD detection techniques based on sub-task generation
Samsung, 2020-2023 -
A machine learning and statistical inference framework for explainable artificial intelligence
Institute of Information & Communications Technology Planning & Evaluation (IITP), 2022-2023 -
A Study on Optimization and Network Interpretation Method for Large-Scale Machine Learning
National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT), 2022-2023