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cs.LG2025
"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift
Harvineet Singh, Fan Xia, Alexej Gossmann +3
Machine learning (ML) models frequently experience performance degradation when deployed in new contexts. Such degradation is rarely uniform: some subgroups may suffer large perfor…
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
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
Jean Feng, Adarsh Subbaswamy, Alexej Gossmann +7
After a machine learning (ML)-based system is deployed, monitoring its performance is important to ensure the safety and effectiveness of the algorithm over time. When an ML algori…