Interpretable AI with Local Distillation
Modern AI models such as tabular foundation models and gradient-boosted ensembles can outpredict classical methods, but provide little basis for reasoning about their predictions. High-stakes decisions call for models that are both accurate and interpretable as built. Local linear modeling offers a path forward: a smooth regression function is locally well approximated by a linear one, allowing a linear fit near each query point to achieve high accuracy without sacrificing transparency. The challenges lie in learning what is "local" and developing statistical tools for interpretation. Here, we propose local distillation, in which a black-box "teacher" guides a regularized linear "student"...
| Authors | Erin Craig, Yiling Huang, Snigdha Panigrahi |
|---|---|
| Primary category | stat.ME |
| Published | 24 August 2026 |
| Last updated at source | 22 September 2026 |
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