110 citations · 132 across the 10 of their papers we have counts for
12 papers
Outlier-Robust Group Inference via Gradient Space Clustering
Yuchen Zeng, Kristjan Greenewald, Kangwook Lee +2
Traditional machine learning models focus on achieving good performance on the overall training distribution, but they often underperform on minority groups. Existing methods can i…
Breaking Fair Binary Classification with Optimal Flipping Attacks
Changhun Jo, Jy-yong Sohn, Kangwook Lee
Minimizing risk with fairness constraints is one of the popular approaches to learning a fair classifier. Recent works showed that this approach yields an unfair classifier if the…
Improved Input Reprogramming for GAN Conditioning
Tuan Dinh, Daewon Seo, Zhixu Du +2
We study the GAN conditioning problem, whose goal is to convert a pretrained unconditional GAN into a conditional GAN using labeled data. We first identify and analyze three approa…
GenLabel: Mixup Relabeling using Generative Models
Jy-yong Sohn, Liang Shang, Hongxu Chen +3
Mixup is a data augmentation method that generates new data points by mixing a pair of input data. While mixup generally improves the prediction performance, it sometimes degrades…
Gradient Inversion with Generative Image Prior
Jinwoo Jeon, Jaechang Kim, Kangwook Lee +2
Federated Learning (FL) is a distributed learning framework, in which the local data never leaves clients devices to preserve privacy, and the server trains models on the data via…
Sample Selection for Fair and Robust Training
Yuji Roh, Kangwook Lee, Steven Euijong Whang +1
Fairness and robustness are critical elements of Trustworthy AI that need to be addressed together. Fairness is about learning an unbiased model while robustness is about learning…