Search Results for author: Yuji Roh

Found 11 papers, 2 papers with code

LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views

no code implementations7 Feb 2024 Yuji Roh, Qingyun Liu, Huan Gui, Zhe Yuan, Yujin Tang, Steven Euijong Whang, Liang Liu, Shuchao Bi, Lichan Hong, Ed H. Chi, Zhe Zhao

By combining two complementing models, LEVI effectively suppresses problematic features in both the fine-tuning data and pre-trained model and preserves useful features for new tasks.

Improving Fair Training under Correlation Shifts

no code implementations5 Feb 2023 Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh

First, we analytically show that existing in-processing fair algorithms have fundamental limits in accuracy and group fairness.


Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective

no code implementations13 Dec 2021 Steven Euijong Whang, Yuji Roh, Hwanjun Song, Jae-Gil Lee

In this survey, we study the research landscape for data collection and data quality primarily for deep learning applications.

BIG-bench Machine Learning Fairness +2

Inspector Gadget: A Data Programming-based Labeling System for Industrial Images

no code implementations7 Apr 2020 Geon Heo, Yuji Roh, Seonghyeon Hwang, Dayun Lee, Steven Euijong Whang

We propose Inspector Gadget, an image labeling system that combines crowdsourcing, data augmentation, and data programming to produce weak labels at scale for image classification.

BIG-bench Machine Learning Data Augmentation +2

FR-Train: A Mutual Information-Based Approach to Fair and Robust Training

1 code implementation ICML 2020 Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh

Trustworthy AI is a critical issue in machine learning where, in addition to training a model that is accurate, one must consider both fair and robust training in the presence of data bias and poisoning.

Data Poisoning Fairness

FR-GAN: Fair and Robust Training

no code implementations25 Sep 2019 Yuji Roh, Kangwook Lee, Gyeong Jo Hwang, Steven Euijong Whang, Changho Suh

We consider the problem of fair and robust model training in the presence of data poisoning.

Attribute Data Poisoning +1

Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach

no code implementations22 Apr 2019 Ki Hyun Tae, Yuji Roh, Young Hun Oh, Hyunsu Kim, Steven Euijong Whang

As machine learning is used in sensitive applications, it becomes imperative that the trained model is accurate, fair, and robust to attacks.

BIG-bench Machine Learning Fairness +1

A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective

no code implementations8 Nov 2018 Yuji Roh, Geon Heo, Steven Euijong Whang

Interestingly, recent research in data collection comes not only from the machine learning, natural language, and computer vision communities, but also from the data management community due to the importance of handling large amounts of data.

BIG-bench Machine Learning Feature Engineering +1

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