5 citations · 10 across the 16 of their papers we have counts for
18 papers
Who's Behind the Text? Robust Model Authorship Attribution
Minseok Ko, Hyunseok Lee, Woomin Song +3
As large language models (LLMs) are increasingly used to generate text, their misuse has become a growing societal problem, with implications for academic misconduct, model distill…
Deep Barycentric Regression for Optimal Transport Map Estimation and its Statistical Optimality
Kunwoong Kim, Insung Kong, Yongdai Kim
The optimal transport (OT) map provides a geometric transformation for aligning probability distributions and has become a useful tool in machine learning. However, existing estima…
COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering
Kyungseon Lee, Hankyo Jeong, Kunwoong Kim +2
Fair clustering aims to make cluster assignments independent of sensitive attributes, but this goal becomes challenging when multiple sensitive attributes jointly define many subgr…
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…
Naive Visual Memory is Not Enough: A Failure-Mode Study of GUI Agents
Seoyoung Choi, Minseok Ko, Hyunseok Lee +4
Graphical User Interface (GUI) agents are increasingly used to automate complex computer tasks across applications, websites, and operating systems. To improve their reliability, r…
Fair Model-based Clustering
Jinwon Park, Kunwoong Kim, Jihu Lee +1
The goal of fair clustering is to find clusters such that the proportion of sensitive attributes (e.g., gender, race, etc.) in each cluster is similar to that of the entire dataset…
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…
Fair Bayesian Model-Based Clustering
Jihu Lee, Kunwoong Kim, Yongdai Kim
Fair clustering has become a socially significant task with the advancement of machine learning technologies and the growing demand for trustworthy AI. Group fairness ensures that…
Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics
Insung Kong, Kunwoong Kim, Yongdai Kim
AI fairness, also known as algorithmic fairness, aims to ensure that algorithms operate without bias or discrimination towards any individual or group. Among various AI algorithms,…
Fair Clustering via Alignment
Kunwoong Kim, Jihu Lee, Sangchul Park +1
Algorithmic fairness in clustering aims to balance the proportions of instances assigned to each cluster with respect to a given sensitive attribute. While recently developed fair…
ReLU integral probability metric and its applications
Yuha Park, Kunwoong Kim, Insung Kong +1
We propose a parametric integral probability metric (IPM) to measure the discrepancy between two probability measures. The proposed IPM leverages a specific parametric family of di…
Fairness Through Matching
Kunwoong Kim, Insung Kong, Jongjin Lee +3
Group fairness requires that different protected groups, characterized by a given sensitive attribute, receive equal outcomes overall. Typically, the level of group fairness is mea…
Distribution-Aware Robust Learning from Long-Tailed Data with Noisy Labels
Jae Soon Baik, In Young Yoon, Kun Hoon Kim +1
Deep neural networks have demonstrated remarkable advancements in various fields using large, well-annotated datasets. However, real-world data often exhibit long-tailed distributi…
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…
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…
SLIDE: a surrogate fairness constraint to ensure fairness consistency
Kunwoong Kim, Ilsang Ohn, Sara Kim +1
As they have a vital effect on social decision makings, AI algorithms should be not only accurate and but also fair. Among various algorithms for fairness AI, learning a prediction…
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…