mlsanityData to 7 Oct 2026

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

arXiv 2401.12272 ↗First posted 22 Jan 2024stat.ML cs.LGcited by 19

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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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 6 of its 34 references that are in the map. First posted on arXiv 22 Jan 2024.

Builds on

6 of 34 references.

References · 6

  1. 2017Adversarial Discriminative Domain Adaptationcited by 1,309
  2. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  3. 2020Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networkscited by 92
  4. 2020Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimalitycited by 89
  5. 2019Transfer Learning for Nonparametric Classification: Minimax Rate and Adaptive Classifiercited by 64
  6. 2020Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Controlcited by 38

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 11; 2026: 3.

19 papers.

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  1. 2024Smoothness Adaptive Hypothesis Transfer Learningcited by 8
  2. 2024Deep Transfer Learning: Model Framework and Error Analysiscited by 4
  3. 2024Transfer Learning for Latent Variable Network Modelscited by 3
  4. 2025Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfercited by 2
  5. 2025Optimal Transfer Learning for Missing Not-at-Random Matrix Completioncited by 2
  6. 2025Wasserstein Transfer Learningcited by 2
  7. 2025Transfer Learning for Benign Overfitting in High-Dimensional Linear Regressioncited by 2
  8. 2024Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Predictioncited by 1
  9. 2025Multi-task Learning for Heterogeneous Multi-source Block-Wise Missing Datacited by 1
  10. 2025Late Fusion Multi-task Learning for Semiparametric Inference with Nuisance Parameterscited by 1
  11. 2025Limitations of refinement methods for weak to strong generalizationcited by 1
  12. 2026Transfer Learning Through Conditional Quantile Matchingcited by 1
7 more
  1. 2024An adaptive transfer learning perspective on classification in non-stationary environmentscited by 0
  2. 2025Transfer Learning through Enhanced Sufficient Representation: Enriching Source Domain Knowledge with Target Datacited by 0
  3. 2025Locally Private Nonparametric Contextual Multi-armed Banditscited by 0
  4. 2025Transfer Learning for Matrix Completioncited by 0
  5. 2025Transfer Learning with Distance Covariance for Random Forest: Error Bounds and an EHR Applicationcited by 0
  6. 2026Transfer Learning in High-dimensional Ising Modelscited by 0
  7. 2026Transfer Learning in Nonparametric Regression with Deep ReLU Networkscited by 0