10 papers
COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering
Kyungseon Lee, Hankyo Jeong, Kunwoong Kim +2
Fair clustering aims to make cluster assignments independent of sensitive attributes, but this goal becomes challenging when multiple sensitive attributes jointly define many subgr…
What Drives the Inlier-Memorization Effect? A Theory of Outlier Detection via Early Training Dynamics
Kunwoong Kim, Dongha Kim
Outlier detection (OD) aims to identify anomalous instances by learning the underlying structure of normal data (inliers), and is particularly challenging in fully unsupervised set…
Naive Visual Memory is Not Enough: A Failure-Mode Study of GUI Agents
Seoyoung Choi, Minseok Ko, Hyunseok Lee +4
Graphical User Interface (GUI) agents are increasingly used to automate complex computer tasks across applications, websites, and operating systems. To improve their reliability, r…
Fair Model-based Clustering
Jinwon Park, Kunwoong Kim, Jihu Lee +1
The goal of fair clustering is to find clusters such that the proportion of sensitive attributes (e.g., gender, race, etc.) in each cluster is similar to that of the entire dataset…
Doubly-Regressing Approach for Subgroup Fairness
Kunwoong Kim, Kyungseon Lee, Jihu Lee +2
Algorithmic fairness is a socially crucial topic in real-world applications of AI. Among many notions of fairness, subgroup fairness is widely studied when multiple sensitive attri…
Fair Clustering via Alignment
Kunwoong Kim, Jihu Lee, Sangchul Park +1
Algorithmic fairness in clustering aims to balance the proportions of instances assigned to each cluster with respect to a given sensitive attribute. While recently developed fair…