Hi! I am a third year PhD student in Machine Learning at Carnegie Mellon University, fortunate to be advised by Zachary Lipton and Bryan Wilder. I'm broadly interested in algorithmic and statistical questions that arise when applying machine learning to society. In 2024, I spent a wonderful summer as an intern on the AI Safety team at Apple. Before that, I received my BS in Physics and BA in Computer Science from Brown University in 2021.
Generate to Discriminate: Expert Routing for Domain Incremental Learning
Yewon Byun, Sanket Vaibhav Mehta, Saurabh Garg, Michael Oberst, Emma Strubell, Bryan Wilder, Zachary Lipton
Earlier version at International Conference on Machine Learning (ICML) Deployable Generative AI, 2023.
[arXiv] [code]
Decision-Aligned Uncertainty Quantification
Santiago Cortes-Gomez, Carlos Patiño, Yewon Byun, Steven Wu, Eric Horvitz, Bryan Wilder
International Conference on Learning Representations (ICLR), 2025.
[arXiv]
Decision-Focused Evaluation of Worst-Case Distribution Shift
Kevin Ren, Yewon Byun, Bryan Wilder
Uncertainty in Artificial Intelligence (UAI), 2024.
[arXiv] [code]
Auditing Fairness under Unobserved Confounding
Yewon Byun, Dylan Sam, Michael Oberst, Zachary Lipton, Bryan Wilder
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.
[arXiv] [code]
Domain Knowledge Priors for Bayesian Neural Networks
Dylan Sam*, Rattana Pukdee*, Daniel Jeong, Yewon Byun, Zico Kolter
International Conference on Machine Learning (ICML) Knowledge and Logical Reasoning, 2023 (oral).
[arXiv]
email: yewonb@cs.cmu.edu
twitter: @yewonbyun_
bluesky: @yewonbyun.bsky.social