Information Theory and Machine Learning Lab
We are a research group headed by Prof. Jy-yong Sohn in the Department of Applied Statistics at Yonsei University.
We focus on research topics in the intersection of information theory and machine learning. More broadly, we delve into various topics in machine learning and artificial intelligence, using mathematical tools from information theory, optimization, learning theory, and probability & statistics. Current research topics include
Foundation Models (including Large Language Models)
Representation Learning
Efficient Machine Learning
For the details of each research topic, please check this page. If you are interested in joining our group, please contact me.
[TMLR'25] Buffer-based Gradient Projection for Continual Federated Learning
[AISTATS'25] A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning
[TMLR'24] Mini-Batch Optimization of Contrastive Loss
[UAI'24] Memorization Capacity for Additive Fine-Tuning with Small ReLU Networks
[AISTATS'24] Analysis of Using Sigmoid Loss for Contrastive Learning
[ICML'23] Looped Transformers as Programmable Computers
[ICLR'23] Equal Improvability: A New Fairness Notion Considering the Long-term Impact
[AAAI'23] Can We Find Strong Lottery Tickets in Generative Models?
Selected Awards & Grants
NRF Korea, Basic Research Lab (기초연구실), 2024 - 2027
NRF Korea, Outstanding Young Scientist (우수신진), 2024 - 2027
Excellence in Teaching Award, Yonsei University, 2023