4 citations · 5 across the 8 of their papers we have counts for
8 papers
META-ANOVA: Screening interactions for interpretable machine learning
Yongchan Choi, Seokhun Park, Chanmoo Park +2
There are two things to be considered when we evaluate predictive models. One is prediction accuracy,and the other is interpretability. Over the recent decades, many prediction mod…
Posterior concentrations of fully-connected Bayesian neural networks with general priors on the weights
Insung Kong, Yongdai Kim
Bayesian approaches for training deep neural networks (BNNs) have received significant interest and have been effectively utilized in a wide range of applications. There have been…
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…