(2023) Score-based generative modeling secretly minimizes the Wasserstein distance (Information Theory and Data Science Workshop, UW Madison SILO)
(2022, 2023) On a bilevel optimization approach to fair classification (USC EE, UCSB CCDC, IOS’22, US-Mexico Workshop on Optimization and its Applications’23)
(2021, 2022) Improving Fairness via Federated Learning (UC Berkeley BLISS, KAIST AI International Symposium)
(2021) Information Theory and Coding for Trustworthy and Scalable Machine Learning (Seoul National University, POSTECH AI)
Recent preprints
Looped Transformers as Programmable Computers Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris Papailiopoulos Arxiv, 2023
Active Learning is a Strong Baseline for Data Subset Selection D. Park, D. Papailiopoulos, K. Lee NeurIPS HITY Workshop, 2022
A Better Way to Decay: Proximal Gradient Training Algorithms for Neural Nets L. Yang, J. Zhang, J. Shenouda, D. Papailiopoulos, K. Lee, and R. Nowak Arxiv, 2022 NeurIPS OPT Workshop, 2022
Outlier-Robust Group Inference via Gradient Space Clustering Y. Zeng, K. Greenewald, K. Lee, J. Solomon, and M. Yurochkin Arxiv, 2022