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cs.LG2026
A Federated Generalized Expectation-Maximization Algorithm for Mixture Models with an Unknown Number of Components
Michael Ibrahim, Nagi Gebraeel, Weijun Xie
We study the problem of federated clustering when the total number of clusters across clients is unknown, and the clients have heterogeneous but potentially overlapping cluster…
cs.LG2025
FDR-SVM: A Federated Distributionally Robust Support Vector Machine via a Mixture of Wasserstein Balls Ambiguity Set
Michael Ibrahim, Heraldo Rozas, Nagi Gebraeel +1
We study a federated classification problem over a network of multiple clients and a central server, in which each client's local data remains private and is subject to uncertainty…
cs.LG2024
Wasserstein Distributionally Robust Multiclass Support Vector Machine
Michael Ibrahim, Heraldo Rozas, Nagi Gebraeel
We study the problem of multiclass classification for settings where data features and their labels are uncertain. We identify that distributionally robus…