mlsanityData to 14 Oct 2026

AdaTrans: Feature-wise and Sample-wise Adaptive Transfer Learning for High-dimensional Regression

arXiv 2403.13565 ↗First posted 20 Mar 2024stat.ML cs.LG math.STcited by 3

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Abstract (arXiv)

We consider the transfer learning problem in the high dimensional linear regression setting, where the feature dimension is larger than the sample size. To learn transferable information, which may vary across features or the source samples, we propose an adaptive transfer learning method that can detect and aggregate the feature-wise (F-AdaTrans) or sample-wise (S-AdaTrans) transferable structures. We achieve this by employing a fused-penalty, coupled with weights that can adapt according to the transferable structure. To choose the weight, we propose a theoretically informed, data-driven procedure, enabling F-AdaTrans to selectively fuse the transferable signals with the target while filtering out non-transferable signals, and S-AdaTrans to obtain the optimal combination of information transferred from each source sample. We show that, with appropriately chosen weights, F-AdaTrans achieves a convergence rate close to that of an oracle estimator with a known transferable structure, and S-AdaTrans recovers existing near-minimax optimal rates as a special case. The effectiveness of the proposed method is validated using both simulation and real data, demonstrating favorable performance compared to the existing methods.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 13 of its 27 references that are in the map. First posted on arXiv 20 Mar 2024.

Builds on

13 of 27 references.

References · 13

  1. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  2. 2020Rethinking Importance Weighting for Deep Learning under Distribution Shiftcited by 93
  3. 2020Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimalitycited by 90
  4. 2021Transfer Learning under High-dimensional Generalized Linear Modelscited by 70
  5. 2020Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Controlcited by 38
  6. 2019Domain Aggregation Networks for Multi-Source Domain Adaptationcited by 37
  7. 2021Personalized Federated Learning: A Unified Framework and Universal Optimization Techniquescited by 34
  8. 2013Generalization Bounds for Domain Adaptationcited by 27
  9. 2019Robust Learning from Untrusted Sourcescited by 27
  10. 2020A One-step Approach to Covariate Shift Adaptationcited by 21
  11. 2021Rethinking Importance Weighting for Transfer Learningcited by 11
  12. 2023Distributed Personalized Empirical Risk Minimizationcited by 6
1 more
  1. 2023Unified Transfer Learning Models in High-Dimensional Linear Regressioncited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guaranteescited by 1
  2. 2025Multi-task Learning for Heterogeneous Multi-source Block-Wise Missing Datacited by 1
  3. 2026Bytes to Threats: Byte-Sequence Transformer For Malware Detectioncited by 0