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arXiv Statistics ML By Erin Craig, Yiling Huang, Snigdha Panigrahi

Interpretable AI with Local Distillation

Read the original on arXiv Statistics ML →

Interpretable AI with Local Distillation proposes a method where a black‑box teacher model guides a regularized linear student model at each query point. The teacher defines locality by upweighting training observations with similar predicted outcomes and anchors the fit with its own prediction at the query point, treated as a pseudo‑observation. By adding Gaussian randomization and refitting, the approach identifies reliable features and stable subgroups, achieving near‑teacher accuracy while producing sparse, locally interpretable linear models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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