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R. Rockafellar

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#machine learning Jul 2026

Directional Influence Function: Estimating Training Data Influence in Constrained Learning

DIF formulates the opti- mality conditions of constrained learning as a variational inequality (VI) and ana- lyzes how perturbing training data affects this VI, establishing DIF as an efficient and reliable tool for data attribution in constrained learning.

Xin Wang, R. Rockafellar, X. Ban · arXiv.org · 0 citations