Residual-as-Teacher: Mitigating Bias Propagation in Student--Teacher Estimation
We study statistical estimation in a student--teacher setting, where predictions from a pre-trained teacher are used to guide a student model. A standard approach is to train the student to directly match the teacher's outputs, which we refer to as student soft matching (SM). This approach directly propagates any systematic bias or mis-specification present in the teacher, thereby degrading the student's predictions. We propose and analyze an alternative scheme, known as residual-as-teacher (RaT), in which the teacher is used to estimate residuals in the student's predictions. Our analysis shows how the student can thereby emulate a proximal gradient scheme for solving an oracle optimizat...
| Authors | Kakei Yamamoto, Martin J. Wainwright |
|---|---|
| Primary category | stat.ML |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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