13 citations · 27 across the 9 of their papers we have counts for
7 papers · 1 filter
A Comprehensive View of Fairness through Distributional Stability
Gayane Taturyan, Charlotte Laclau, Stephan Clémencon
We view fairness as a property of distributional stability. Rather than assessing a predictor under a fixed data distribution, we study how its predictions change under perturbatio…
Drop the mask! GAMM-A Taxonomy for Graph Attributes Missing Mechanisms
Richard Serrano, Baptiste Jeudy, Charlotte Laclau +1
Exploring missing data in attributed graphs introduces unique challenges beyond those found in tabular datasets. In this work, we extend the taxonomy for missing data mechanisms to…
From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport
Quentin Bouniot, Ievgen Redko, Anton Mallasto +6
In the last decade, we have witnessed the introduction of several novel deep neural network (DNN) architectures exhibiting ever-increasing performance across diverse tasks. Explain…
A Survey on Fairness for Machine Learning on Graphs
Charlotte Laclau, Christine Largeron, Manvi Choudhary
Nowadays, the analysis of complex phenomena modeled by graphs plays a crucial role in many real-world application domains where decisions can have a strong societal impact. However…
All of the Fairness for Edge Prediction with Optimal Transport
Charlotte Laclau, Ievgen Redko, Manvi Choudhary +1
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, ed…
Deep Neural Networks Are Congestion Games: From Loss Landscape to Wardrop Equilibrium and Beyond
Nina Vesseron, Ievgen Redko, Charlotte Laclau
The theoretical analysis of deep neural networks (DNN) is arguably among the most challenging research directions in machine learning (ML) right now, as it requires from scientists…
Rank-one partitioning: formalization, illustrative examples, and a new cluster enhancing strategy
Charlotte Laclau, Franck Iutzeler, Ievgen Redko
In this paper, we introduce and formalize a rank-one partitioning learning paradigm that unifies partitioning methods that proceed by summarizing a data set using a single vector t…