1 citations · 1 across the 5 of their papers we have counts for
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Learning Across Heterogeneous Feature Spaces with Group Robustness
Xenia Konti, Arjun Tschand, Chuan Hong +2
In clinical applications, patients undergo different combinations of tests, and machine learning models must therefore ensure accurate predictions across heterogeneous feature spac…
Group Distributionally Robust Machine Learning under Group Level Distributional Uncertainty
Xenia Konti, Yi Shen, Zifan Wang +4
The performance of machine learning (ML) models critically depends on the quality and representativeness of the training data. In applications with multiple heterogeneous data gene…
FairPOT: Balancing AUC Performance and Fairness with Proportional Optimal Transport
Pengxi Liu, Yi Shen, Matthew M. Engelhard +4
Fairness metrics utilizing the area under the receiver operator characteristic curve (AUC) have gained increasing attention in high-stakes domains such as healthcare, finance, and…
Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare
Xenia Konti, Hans Riess, Manos Giannopoulos +4
In this paper, we address the challenge of heterogeneous data distributions in cross-silo federated learning by introducing a novel algorithm, which we term Cross-silo Robust Clust…