20 citations · 22 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 20 cited
Fair Oversampling Technique using Heterogeneous Clusters
Ryosuke Sonoda
Class imbalance and group (e.g., race, gender, and age) imbalance are acknowledged as two reasons in data that hinder the trade-off between fairness and utility of machine learning…
cs.LG2021★ 2 cited
A Pre-processing Method for Fairness in Ranking
Ryosuke Sonoda
Fair ranking problems arise in many decision-making processes that often necessitate a trade-off between accuracy and fairness. Many existing studies have proposed correction metho…