Search Results for author: Christine Largeron

Found 5 papers, 2 papers with code

A Survey on Fairness for Machine Learning on Graphs

2 code implementations11 May 2022 Charlotte Laclau, Christine Largeron, Manvi Choudhary

In that context, algorithmic contributions for graph mining are not spared by the problem of fairness and present some specific challenges related to the intrinsic nature of graphs: (1) graph data is non-IID, and this assumption may invalidate many existing studies in fair machine learning, (2) suited metric definitions to assess the different types of fairness with relational data and (3) algorithmic challenge on the difficulty of finding a good trade-off between model accuracy and fairness.

BIG-bench Machine Learning Fairness +2

All of the Fairness for Edge Prediction with Optimal Transport

no code implementations30 Oct 2020 Charlotte Laclau, Ievgen Redko, Manvi Choudhary, Christine Largeron

Machine learning and data mining algorithms have been increasingly used recently to support decision-making systems in many areas of high societal importance such as healthcare, education, or security.

Attribute Decision Making +1

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