Differential subgroups identify specific feature combinations where population differences in outcomes are most extreme, found via a new optimization objective and the DiffSub method.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2representative citing papers
CanniUplift mitigates cross-seller substitution and non-redemption noise in e-commerce uplift modeling via a global GMV alignment loss and a redemption-based outcome decomposition, improving platform-wide incremental GMV.
citing papers explorer
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Differential Subgroup Discovery: Characterizing Where Two Populations Differ, and Why
Differential subgroups identify specific feature combinations where population differences in outcomes are most extreme, found via a new optimization objective and the DiffSub method.
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CanniUplift: A Holistic Framework for Mitigating Seller and Incentive Cannibalization in E-commerce Uplift Modeling
CanniUplift mitigates cross-seller substitution and non-redemption noise in e-commerce uplift modeling via a global GMV alignment loss and a redemption-based outcome decomposition, improving platform-wide incremental GMV.