Exact and Certified Data Shapley for Weighted k-Nearest-Neighbor Regression and Soft-Label Prediction

Jul 15, 2026

Researchers introduce the first pseudo-polynomial-time exact algorithm for computing Data Shapley values in weighted k-nearest-neighbor (KNN) regression, addressing a longstanding computational barrier. The work also presents a certified fully polynomial-time approximation scheme (FPTAS) with machine-checkable error bounds and extends the approach to soft-label multi-class prediction. An open-source implementation and the first exact ground-truth dataset for weighted-regression Data Shapley are provided.

Why it matters: This advance enables deterministic, certified data valuation in weighted KNN regression, providing a reliable reference for auditing and improving approximate methods.

Full story at: arXiv Machine Learning ↗