4 citations · 5 across the 7 of their papers we have counts for
7 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…
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference
Insung Kong, Dongyoon Yang, Jongjin Lee +3
Bayesian approaches for learning deep neural networks (BNN) have been received much attention and successfully applied to various applications. Particularly, BNNs have the merit of…
Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples
Dongyoon Yang, Insung Kong, Yongdai Kim
Adversarial training, which is to enhance robustness against adversarial attacks, has received much attention because it is easy to generate human-imperceptible perturbations of da…
Masked Bayesian Neural Networks : Computation and Optimality
Insung Kong, Dongyoon Yang, Jongjin Lee +2
As data size and computing power increase, the architectures of deep neural networks (DNNs) have been getting more complex and huge, and thus there is a growing need to simplify su…