4 papers
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…
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…
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation
Dongyoon Yang, Insung Kong, Yongdai Kim
Adversarial robustness is a research area that has recently received a lot of attention in the quest for trustworthy artificial intelligence. However, recent works on adversarial r…
Improving Performance of Semi-Supervised Learning by Adversarial Attacks
Dongyoon Yang, Kunwoong Kim, Yongdai Kim
Semi-supervised learning (SSL) algorithm is a setup built upon a realistic assumption that access to a large amount of labeled data is tough. In this study, we present a generalize…