13 citations · 20 across the 9 of their papers we have counts for
10 papers
What Drives the Inlier-Memorization Effect? A Theory of Outlier Detection via Early Training Dynamics
Kunwoong Kim, Dongha Kim
Outlier detection (OD) aims to identify anomalous instances by learning the underlying structure of normal data (inliers), and is particularly challenging in fully unsupervised set…
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…
ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models
Dongha Kim, Jaesung Hwang, Jongjin Lee +2
The unsupervised outlier detection (UOD) problem refers to a task to identify inliers given training data which contain outliers as well as inliers, without any labeled information…
Learning fair representation with a parametric integral probability metric
Dongha Kim, Kunwoong Kim, Insung Kong +2
As they have a vital effect on social decision-making, AI algorithms should be not only accurate but also fair. Among various algorithms for fairness AI, learning fair representati…
INN: A Method Identifying Clean-annotated Samples via Consistency Effect in Deep Neural Networks
Dongha Kim, Yongchan Choi, Kunwoong Kim +1
In many classification problems, collecting massive clean-annotated data is not easy, and thus a lot of researches have been done to handle data with noisy labels. Most recent stat…
A likelihood approach to nonparametric estimation of a singular distribution using deep generative models
Minwoo Chae, Dongha Kim, Yongdai Kim +1
We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models. More specifically, a deep genera…
Kernel-convoluted Deep Neural Networks with Data Augmentation
Minjin Kim, Young-geun Kim, Dongha Kim +2
The Mixup method (Zhang et al. 2018), which uses linearly interpolated data, has emerged as an effective data augmentation tool to improve generalization performance and the robust…
Understanding and Improving Virtual Adversarial Training
Dongha Kim, Yongchan Choi, Yongdai Kim
In semi-supervised learning, virtual adversarial training (VAT) approach is one of the most attractive method due to its intuitional simplicity and powerful performances. VAT finds…
On variation of gradients of deep neural networks
Yongdai Kim, Dongha Kim
We provide a theoretical explanation of the role of the number of nodes at each layer in deep neural networks. We prove that the largest variation of a deep neural network with ReL…
Fast convergence rates of deep neural networks for classification
Yongdai Kim, Ilsang Ohn, Dongha Kim
We derive the fast convergence rates of a deep neural network (DNN) classifier with the rectified linear unit (ReLU) activation function learned using the hinge loss. We consider t…