Xin Wang

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.