Lee Lab @ UW Madison

Research focus: Theory and algorithms for foundation models.

Research Highlights

Arxiv’24 / Parameter-Efficient Fine-Tuning of State Space Models / Summary / Github
Arxiv’24 / Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition / Summary
Arxiv’24 / ENTP: Encoder-only Next Token Prediction / Summary / Github
Arxiv’24 / Looped Transformers for Length Generalization / Summary
ICML’24 / Dual operating modes of ICL / Summary / Github
ICLR’24 / Expressive power of LoRA / Summary / Github
NeurIPS’23 / RL for (text-to-image) diffusion model finetuning / Summary / Github
ICML’23 / RL for diffusion model finetuning / Summary / Github
NeurIPS’22 / Finetuning LLMs on non-textual data / Summary / Github

We sincerely appreciate the support provided by our sponsors: NSF, Amazon, and FuriosaAI.

[postdocs] We are looking for a postdoc interested in the theoretical and algorithmic aspects of foundation models, particularly LLMs. If you want to work with us, please email me your CV and a research statement. I strongly recommend reading our lab’s recent papers.
[PhD students] We are looking for PhD students (ECE or CS) interested in the theoretical and algorithmic aspects of foundation models, particularly LLMs. If you want to work with us, please email me your CV and a research statement. I strongly recommend reading our lab’s recent papers.
[MS students] I am not currently looking for MS students.
[undergraduate students] You might want to consider taking my class first. I teach various machine learning courses (ECE 532, 539, 561, 570, 761, …).

(Nov. 2024) Dr. Jungtaek Kim joined our lab as a postdoc! Welcome!
(July 2024) [COLM’24] Can MLLMs Perform Text-to-Image In-Context Learning?
(June 2024) [Arxiv’24] Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data / Summary
(June 2024) [TMLR’24] Mini-Batch Optimization of Contrastive Loss
(April 2024) [UAI’24] Memorization capacity of additive fine-Tuning
(Apr. 2024) Amazon Research Awards
Our group will develop principled approaches to prompt engineering through the information/coding-theoretic lens. Huge thanks to Amazon and my amazing collaborators and students!
(Mar. 2024) NSF CAREER Award
Our group will develop a unified theory and new algorithms with provable guarantees for learning with frozen pretrained models, also known as foundation models. Huge thanks to NSF and my amazing collaborators and students!

(Apr. 2024) The Johns Hopkins University CIS/MINDS seminar
Title: Theoretical Exploration of Foundation Model Adaptation Methods
(Mar. 2024) The 58th Annual Conference on Information Sciences and Systems @ Princeton University
Title: A Probabilistic Framework for Understanding In-Context Task Learning and Retrieval
(Feb. 2024) 2024 Information Theory and Applications Workshop
Title: The Expressive Power of Low-Rank Adaptation (LoRA)
(Feb. 2024) Foundations of Data Science - Virtual Talk Series @ UCSD/NSF TRIPODS Institute on Emerging CORE Methods in Data Science (EnCORE)
Title: Theoretical Exploration of Foundation Model Adaptation Methods
(Dec. 2023) CSP Seminar @ University of Michigan
Title: Towards a Theoretical Understanding of Parameter-Efficient Fine-Tuning (and Beyond)
(Nov. 2023) Efficient ML workshop @ Google Research New York
Title: The Expressive Power of Low-Rank Adaptation (LoRA)