SMART Fine-tuning Factor Augmented Neural Lasso
Fine-tuning is a widely used strategy for adapting pre-trained models to new tasks, yet its methodology and theoretical properties in high-dimensional nonparametric settings with variable selection have not yet been developed. We propose a source-model-augmented residual tuning (SMART) framework, which incorporates the pre-trained source model as an augmented feature into the target learner and estimates only the residual target-specific component. The approach is widely applicable, from parametric and sparse models to neural networks and blackbox machine learning models. We focus on the development of fine-tuning factor-augmented neural Lasso, resulting in SMART-FAN-Lasso. This transfer-...
| Authors | Jinhang Chai, Jianqing Fan, Cheng Gao et al. |
|---|---|
| Primary category | stat.ML |
| Published | 14 April 2026 |
| Last updated at source | 17 May 2026 |
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