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cs.LG2026
How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?
Julia Kostin, Kasra Jalaldoust, Elias Bareinboim +2
Machine learning models often degrade when they are deployed on a target distribution that differs from the source distributions they were trained on. Recent work in causality-base…
cs.LG2025
Neyman-Pearson Classification under Both Null and Alternative Distributions Shift
Mohammadreza M. Kalan, Yuyang Deng, Eitan J. Neugut +1
We consider the problem of transfer learning in Neyman-Pearson classification, where the objective is to minimize the error w.r.t. a distribution , subject to the constraint…
cs.LG2025
Transfer Neyman-Pearson Algorithm for Outlier Detection
Mohammadreza M. Kalan, Eitan J. Neugut, Samory Kpotufe
We consider the problem of transfer learning in outlier detection where target abnormal data is rare. While transfer learning has been considered extensively in traditional balance…