Lee Lab @ UW Madison

Research focus

Building Modular Deep Learning Systems with Pretrained Components (Large Language Models, Diffusion Models, …)
- LIFT: Solving non-language ML tasks with language models. NeurIPS’22 [Github] [Summary]
- Designing a CPU with a looped Transformer. ICML’23 [Summary]
- Designing a chatbot for long open-domain conversation with a inter-connected language models. ACL’23 (Findings) [Github] [Summary]
- Reinforcment learning for finetuning text-to-image model. Arxiv’23 [Summary]
- Reinforcment learning for short-cut DDPM sampling. ICML’23 [Summary]
- Diffusion models minimize the Wasserstein distance. NeurIPS’22 [Summary]
- WALIP: Connecting two CLIP modles for word translation. EMNLP’22 (Findings) [Github] [Summary]
- Invited talks (2023) The Coordinated Science Laboratory Student Conference, Information Theory and Applications Workshop, Information Theory and Data Science Workshop, UW Madison SILO, Midwest Regional Conferenc, (2022) Samsung Advanced Institute of Technology, EIRIC Korea
ML Fairness
- Equal Improvability ICLR’23 (Github) [Summary]
- FairBatch ICLR’21, its robust variant NeurIPS’21, and its decentralized variant MLSys-CrossFL 2022
- Fundamental limits of local fair training in federated learning. [ISIT’23]
- Fair training under distribution shift. ICML’23
- Invited talks (2023) US-Mexico Workshop on Optimization and its Applications, (2022) KAIST AI International Symposium, IOS’22, UCSB CCDC, USC EE, (2021) UC Berkeley BLISS
Coded computation
- Coded-InvNet (coded computation for deep invertible neural networks): ICML’21
- Speeding Up Distributed Machine Learning Using Codes: T-IT’18
  - The Joint Communications Society/Information Theory Society Paper Award, 2020
- Invited talks (2021) Seoul National University, POSTECH AI

New preprints

Vector-Valued Variation Spaces and Width Bounds for DNNs: Insights on Weight Decay Regularization
Joseph Shenouda, Rahul Parhi, Kangwook Lee, and Robert D. Nowak
Arxiv, 2023
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, Kangwook Lee, Kimin Lee
Arxiv, 2023
A Better Way to Decay: Proximal Gradient Training Algorithms for Neural Nets
Liu Yang, Jifan Zhang, Joseph Shenouda, Dimitris Papailiopoulos, Kangwook Lee, and Robert D. Nowak
Arxiv, NeurIPS’22 OPT Workshop, 2022
Outlier-Robust Group Inference via Gradient Space Clustering
Yuchen Zeng, Kristjan Greenewald, Kangwook Lee, Justin Solomon, Mikhail Yurochkin
Arxiv, 2022

News

(May 2023) [ACL’23 (Findings)]
- Prompted LLMs as Chatbot Modules for Long Open-domain Conversation
(Apr. 2023) [ICML’23]
(Apr. 2023) Bryce Chen received the 2023–2024 Wisconsin Hilldale Undergraduate/Faculty Research Fellowship! Congratulations!
(Apr. 2023) [ISIT’23]
- Federated Learning with Local Fairness Constraints
(Mar. 2023) Dr. Dinh will join UCSF as a postdoc! Congratulations!
(Mar. 2023) [ICLR’23 Workshop on Mathematical and Empirical Understanding of Foundation Models]
- Mini-Batch Optimization of Contrastive Loss
- Looped Transformers as Programmable Computers
(Feb. 2023) Prof. Lee gave/will give invited talks.
- Midwest Regional Conference (Mar. 2023)
- The Coordinated Science Laboratory Student Conference (Feb. 2023)
- Information Theory and Applications Workshop (Feb. 2023)
- Information Theory and Data Science Workshop (Jan. 2023)
- The US-Mexico Workshop on Optimization and its Applications (Jan. 2023)
(Jan. 2023) [ICLR’23]
- Equal Improvability: A New Fairness Notion Considering the Long-Term Impact
(Dec. 2022) Dr. Sohn will join Yonsei University as an assistant professor! Congratulations!
(July. 2022) Prof. Lee received UW Madison ECE Grainger Faculty Scholarship Award.
(Mar. 2022) Prof. Lee received 2022 KSEA Young Investigator Grants.