6 papers
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…
Bayesian Neural Networks for Functional ANOVA model
Seokhun Park, Choeun Kim, Jihu Lee +3
With the increasing demand for interpretability in machine learning, functional ANOVA decomposition has gained renewed attention as a principled tool for breaking down high-dimensi…
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…
Fair Bayesian Model-Based Clustering
Jihu Lee, Kunwoong Kim, Yongdai Kim
Fair clustering has become a socially significant task with the advancement of machine learning technologies and the growing demand for trustworthy AI. Group fairness ensures that…
TAROT: Towards Essentially Domain-Invariant Robustness with Theoretical Justification
Dongyoon Yang, Jihu Lee, Yongdai Kim
Robust domain adaptation against adversarial attacks is a critical research area that aims to develop models capable of maintaining consistent performance across diverse and challe…