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Lee Lab @ UW Madison

Research focus

  • Deep Learning with Large Pretrained Models (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]
    • Teaching arithmetic to a small Transformer. Arxiv’23 [Github] [Summary]
    • A compute-latency trade-off for language model decoding. Arxiv’23
    • Training CLIP models with mini-batches. Arxiv’23 [Github]
    • 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. NeurIPS’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 models for word translation. EMNLP’22 (Findings) [Github] [Summary]
    • Invited talks on LLMs/GPT/Transformers The Future Frontier of Large Language Models — A UW-Madison Panel Discussion (June 2023), BarryFest (June 2023), Innovation in Data Seminar @ Early Warning (June 2023), KSEA Distinguished Guest Series (May 2023), The second annual Wisconsin Digital Symposium (May 2023), University of Wisconsin-Madison Law School (May 2023), Midwest Regional Conference (Mar. 2023), Samsung Advanced Institute of Technology (Aug. 2022), EIRIC Korea (July 2022)
    • Invited talks on Diffusion Models The Machine Learning for Medical Imaging (ML4MI) at UW-Madison (Sept. 2023), The Coordinated Science Laboratory Student Conference (Feb. 2023), Information Theory and Applications Workshop (Feb. 2023), Information Theory and Data Science Workshop (Feb. 2023), UW Madison SILO (Jan. 2023), UW Madison Physics Department (Jan. 2022)
  • ML Fairness
    • Equal Improvability ICLR’23 (Github) [Summary] [Montreal AI Ethics Institute]
    • 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 The Fairness and Ethics in ML Seminars at AmFam (Aug. 2023), The US-Mexico Workshop on Optimization and its Applications (Jan. 2023), KAIST CS Colloquium (Sept. 2022), USC EE (Apr. 2022), UCSB CCDC Seminar Series (Apr. 2022), KAIST EE Colloquium Lecture Series (Mar. 2022), UC Berkeley BLISS (Feb. 2022), IOS’22, KAIST AI International Symposium (Dec. 2021), AI+Society seminar @ University of Wisconsin (Oct. 2021), POSTECH (June 2021), Shannon meets Turing Colloquium @ Seoul National University (May 2021), Machine Learning Ideas @ Microsoft Research New England (Dec. 2020), UW Madison SILO (Nov. 2020), UC Berkeley BLISS (Nov. 2020)

New preprints