1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2024
DomainLab: A modular Python package for domain generalization in deep learning
Xudong Sun, Carla Feistner, Alexej Gossmann +10
Poor generalization performance caused by distribution shifts in unseen domains often hinders the trustworthy deployment of deep neural networks. Many domain generalization techniq…
cs.LG2024★ 1 cited
A hierarchical decomposition for explaining ML performance discrepancies
Jean Feng, Harvineet Singh, Fan Xia +2
Machine learning (ML) algorithms can often differ in performance across domains. Understanding their performance differs is crucial for determining what types of int…
cs.LG2023★ 1 cited
Is this model reliable for everyone? Testing for strong calibration
Jean Feng, Alexej Gossmann, Romain Pirracchio +3
In a well-calibrated risk prediction model, the average predicted probability is close to the true event rate for any given subgroup. Such models are reliable across heterogeneous…