Evelyn Ma — accepted AI-conference papers

2024

FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning

Evelyn Ma, Chao Pan, S. Rasoul Etesami, Han Zhao, Olgica Milenkovic

NeurIPS 2024poster

The performance of Transfer Learning (TL) significantly depends on effective pretraining, which not only requires extensive amounts of data but also substantial computational resources. As a result, in practice, it is challenging to successfully perform TL at the level of individual model developers…

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2022

Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization

Xiaojun Xu, Jacky Y Zhang, Evelyn Ma, Hyun Ho Son, Sanmi Koyejo, Bo Li

ICML 2022spotlight

Machine learning (ML) robustness and domain generalization are fundamentally correlated: they essentially concern data distribution shifts under adversarial and natural settings, respectively. On one hand, recent studies show that more robust (adversarially trained) models are more generalizable. On…

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