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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★ 1 cited
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…