activity
20182022
most citedMatrix Completion with Hierarchical Graph Side Information

9 citations · 11 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG20211 cited

Sample Selection for Fair and Robust Training

Yuji Roh, Kangwook Lee, Steven Euijong Whang +1

Fairness and robustness are critical elements of Trustworthy AI that need to be addressed together. Fairness is about learning an unbiased model while robustness is about learning…

cs.LG2020

FairBatch: Batch Selection for Model Fairness

Yuji Roh, Kangwook Lee, Steven Euijong Whang +1

Training a fair machine learning model is essential to prevent demographic disparity. Existing techniques for improving model fairness require broad changes in either data preproce…

cs.LG2020

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

Yuji Roh, Kangwook Lee, Steven Euijong Whang +1

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 d…

cs.IT2019

Community Detection and Matrix Completion with Social and Item Similarity Graphs

Qiaosheng Zhang, Vincent Y. F. Tan, Changho Suh

We consider the problem of recovering a binary rating matrix as well as clusters of users and items based on a partially observed matrix together with side-information in the form…

quant-ph2018

Longer distance continuous variable quantum key distribution protocol with photon subtraction at receiver

Kyongchun Lim, Changho Suh, June-Koo Kevin Rhee

One of the limitation of continuous variable quantum key distribution is relatively short transmission distance of secure keys. In order to overcome the limitation, some solutions…