Transfer Learning in Nonparametric Regression with Deep ReLU Networks
This paper develops a general transfer learning framework for nonparametric regression with data consisting of multiple groups. Under the assumption that groups share a common structure along with group-specific deviations in additive form, the proposed method employs a two-stage offset learning procedure: the first stage pools data from all groups to estimate an overall mean function, and the second stage estimates offsets for each group, yielding final group-level estimators through additive combination. Upper bounds on the $\mathcal L_2$ error are established for the proposed framework, covering a broad class of nonparametric estimators under mild complexity and noise conditions. When ...
| Authors | Junpeng Ren, Carlos Misael Madrid Padilla, Yanzhen Chen et al. |
|---|---|
| Primary category | stat.ML |
| Published | 20 August 2026 |
| Last updated at source | 20 August 2026 |
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