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stat.ML2025
Private Synthetic Graph Generation and Fused Gromov-Wasserstein Distance
Leoni Carla Wirth, Gholamali Aminian, Gesine Reinert
Networks are popular for representing complex data. In particular, differentially private synthetic networks are much in demand for method and algorithm development. The network ge…
stat.ML2024
Generalization and Robustness of the Tilted Empirical Risk
Gholamali Aminian, Amir R. Asadi, Tian Li +3
The generalization error (risk) of a supervised statistical learning algorithm quantifies its prediction ability on previously unseen data. Inspired by exponential tilting, \citet{…