# Transfer Learning for Nonparametric Regression: Non-asymptotic Minimax Analysis and Adaptive Procedure

arXiv: https://arxiv.org/abs/2401.12272 · HTML: https://mlsanity.com/paper/2401.12272/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 22 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks](https://mlsanity.com/paper/2003.11617/) (2020)
- [Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimality](https://mlsanity.com/paper/2006.10593/) (2020)
- [Transfer Learning for Nonparametric Classification: Minimax Rate and Adaptive Classifier](https://mlsanity.com/paper/1906.02903/) (2019)
- [Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control](https://mlsanity.com/paper/2010.11037/) (2020)

## Built on by (most cited first, 22 of 22)

- [Smoothness Adaptive Hypothesis Transfer Learning](https://mlsanity.com/paper/2402.14966/) (2024)
- [Deep Transfer Learning: Model Framework and Error Analysis](https://mlsanity.com/paper/2410.09383/) (2024)
- [Transfer Learning for Latent Variable Network Models](https://mlsanity.com/paper/2406.03437/) (2024)
- [Transfer Learning for Benign Overfitting in High-Dimensional Linear Regression](https://mlsanity.com/paper/2510.15337/) (2025)
- [Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfer](https://mlsanity.com/paper/2501.10870/) (2025)
- [Optimal Transfer Learning for Missing Not-at-Random Matrix Completion](https://mlsanity.com/paper/2503.00174/) (2025)
- [Wasserstein Transfer Learning](https://mlsanity.com/paper/2505.17404/) (2025)
- [Late Fusion Multi-task Learning for Semiparametric Inference with Nuisance Parameters](https://mlsanity.com/paper/2507.07941/) (2025)
- [Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction](https://mlsanity.com/paper/2405.15600/) (2024)
- [Transfer Learning through Enhanced Sufficient Representation: Enriching Source Domain Knowledge with Target Data](https://mlsanity.com/paper/2502.20414/) (2025)
- [Multi-task Learning for Heterogeneous Multi-source Block-Wise Missing Data](https://mlsanity.com/paper/2505.24413/) (2025)
- [Limitations of refinement methods for weak to strong generalization](https://mlsanity.com/paper/2508.17018/) (2025)
- [Transfer Learning Through Conditional Quantile Matching](https://mlsanity.com/paper/2602.02358/) (2026)
- [An adaptive transfer learning perspective on classification in non-stationary environments](https://mlsanity.com/paper/2405.18091/) (2024)
- [Locally Private Nonparametric Contextual Multi-armed Bandits](https://mlsanity.com/paper/2503.08098/) (2025)
- [Transfer Learning for Matrix Completion](https://mlsanity.com/paper/2507.02248/) (2025)
- [Transfer Learning with Distance Covariance for Random Forest: Error Bounds and an EHR Application](https://mlsanity.com/paper/2510.10870/) (2025)
- [SMART Fine-tuning Factor Augmented Neural Lasso](https://mlsanity.com/paper/2604.12288/) (2026)
- [Transfer Learning in High-dimensional Ising Models](https://mlsanity.com/paper/2607.03005/) (2026)
- [Black-Box Knowledge Transfer across Distinct Feature Sets](https://mlsanity.com/paper/2608.12403/) (2026)
- [Transfer Learning in Nonparametric Regression with Deep ReLU Networks](https://mlsanity.com/paper/2608.20255/) (2026)
- [Domain Adaptation with Target Information via Doubly-Anchored Distributionally Robust Optimization](https://mlsanity.com/paper/2609.32730/) (2026)

## Abstract (arXiv)

Transfer learning for nonparametric regression is considered. We first study the non-asymptotic minimax risk for this problem and develop a novel estimator called the confidence thresholding estimator, which is shown to achieve the minimax optimal risk up to a logarithmic factor. Our results demonstrate two unique phenomena in transfer learning: auto-smoothing and super-acceleration, which differentiate it from nonparametric regression in a traditional setting. We then propose a data-driven algorithm that adaptively achieves the minimax risk up to a logarithmic factor across a wide range of parameter spaces. Simulation studies are conducted to evaluate the numerical performance of the adaptive transfer learning algorithm, and a real-world example is provided to demonstrate the benefits of the proposed method.