37 citations · 88 across the 10 of their papers we have counts for
6 papers · 1 filter
Improving Fair Training under Correlation Shifts
Yuji Roh, Kangwook Lee, Steven Euijong Whang +1
Model fairness is an essential element for Trustworthy AI. While many techniques for model fairness have been proposed, most of them assume that the training and deployment data di…
Equal Experience in Recommender Systems
Jaewoong Cho, Moonseok Choi, Changho Suh
We explore the fairness issue that arises in recommender systems. Biased data due to inherent stereotypes of particular groups (e.g., male students' average rating on mathematics i…
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…
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…
MC2G: An Efficient Algorithm for Matrix Completion with Social and Item Similarity Graphs
Qiaosheng Zhang, Geewon Suh, Changho Suh +1
In this paper, we design and analyze MC2G (Matrix Completion with 2 Graphs), an algorithm that performs matrix completion in the presence of social and item similarity graphs. MC2G…
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…