1 citations · 1 across the 17 of their papers we have counts for
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Optimal Transport under Group Fairness Constraints
Linus Bleistein, Mathieu Dagréou, Francisco Andrade +2
Ensuring fairness in matching algorithms is a key challenge in allocating scarce resources and positions. Focusing on Optimal Transport (OT), we introduce a novel notion of group f…
Principled Federated Random Forests for Heterogeneous Data
Rémi Khellaf, Erwan Scornet, Aurélien Bellet +1
Random Forests (RF) are among the most powerful and widely used predictive models for centralized tabular data, yet few methods exist to adapt them to the federated learning settin…
Differentially Private and Federated Structure Learning in Bayesian Networks
Ghita Fassy El Fehri, Aurélien Bellet, Philippe Bastien
Learning the structure of a Bayesian network from decentralized data poses two major challenges: (i) ensuring rigorous privacy guarantees for participants, and (ii) avoiding commun…
Optimal Transport with Heterogeneously Missing Data
Linus Bleistein, Aurélien Bellet, Julie Josse
We consider the problem of solving the optimal transport problem between two empirical distributions with missing values. Our main assumption is that the data is missing completely…
Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis
Rémi Khellaf, Aurélien Bellet, Julie Josse
We study Federated Causal Inference, an approach to estimate treatment effects from decentralized data across centers. We compare three classes of Average Treatment Effect (ATE) es…