Research Topics
I am interested in the theoretical foundations that explain when and why machine learning methods succeed. My current research focuses on:
Natural Language Processing
Representation Learning
Self-Supervised Learning, Multi-modal Learning, Contrastive Learning
Synthetic Data Generation
Generalized Linear Models
Publications
On the Similarities of Embeddings in Contrastive Learning [arxiv] [github]
Chungpa Lee, Sehee Lim, Kibok Lee, Jy-yong Sohn
In Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025
A Generalized Theory of Mixup for Structure-Preserving Synthetic Data [paper] [arxiv] [github]
Chungpa Lee, Jongho Im, Joseph H.T. Kim
In Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning [paper] [arxiv] [github]
Chungpa Lee, Jeongheon Oh, Kibok Lee, Jy-yong Sohn
In Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Analysis of Using Sigmoid Loss for Contrastive Learning [paper] [arxiv] [github]
Chungpa Lee, Joonhwan Chang, Jy-yong Sohn
In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS), 2024