135 citations · 298 across the 23 of their papers we have counts for
24 papers
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…
A Bayesian sparse factor model with adaptive posterior concentration
Ilsang Ohn, Lizhen Lin, Yongdai Kim
In this paper, we propose a new Bayesian inference method for a high-dimensional sparse factor model that allows both the factor dimensionality and the sparse structure of the load…
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…
Covariate balancing using the integral probability metric for causal inference
Insung Kong, Yuha Park, Joonhyuk Jung +2
Weighting methods in causal inference have been widely used to achieve a desirable level of covariate balancing. However, the existing weighting methods have desirable theoretical…
Within-group fairness: A guidance for more sound between-group fairness
Sara Kim, Kyusang Yu, Yongdai Kim
As they have a vital effect on social decision-making, AI algorithms not only should be accurate and but also should not pose unfairness against certain sensitive groups (e.g., non…