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cs.LG2025
On the Normalization of Confusion Matrices: Methods and Geometric Interpretations
Johan Erbani, Pierre-Edouard Portier, Elod Egyed-Zsigmond +2
The confusion matrix is a standard tool for evaluating classifiers by providing insights into class-level errors. In heterogeneous settings, its values are shaped by two main facto…
cs.LG2025
A Weighted Loss Approach to Robust Federated Learning under Data Heterogeneity
Johan Erbani, Sonia Ben Mokhtar, Pierre-Edouard Portier +2
Federated learning (FL) is a machine learning paradigm that enables multiple data holders to collaboratively train a machine learning model without sharing their training data with…