mlsanityData to 14 Oct 2026

Transfer Learning for Nonparametric Classification: Minimax Rate and Adaptive Classifier

arXiv 1906.02903 ↗First posted 7 Jun 2019math.ST cs.LG stat.MEcited by 65

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Abstract (arXiv)

Human learners have the natural ability to use knowledge gained in one setting for learning in a different but related setting. This ability to transfer knowledge from one task to another is essential for effective learning. In this paper, we study transfer learning in the context of nonparametric classification based on observations from different distributions under the posterior drift model, which is a general framework and arises in many practical problems. We first establish the minimax rate of convergence and construct a rate-optimal two-sample weighted K-NN classifier. The results characterize precisely the contribution of the observations from the source distribution to the classification task under the target distribution. A data-driven adaptive classifier is then proposed and is shown to simultaneously attain within a logarithmic factor of the optimal rate over a large collection of parameter spaces. Simulation studies and real data applications are carried out where the numerical results further illustrate the theoretical analysis. Extensions to the case of multiple source distributions are also considered.

Key facts. Cited by 65 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Jun 2019.

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Citing papers per year: 2021: 5; 2022: 13; 2023: 11; 2024: 14; 2025: 15; 2026: 7.

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  1. 2021Transfer Learning under High-dimensional Generalized Linear Modelscited by 70
  2. 2022Optimally tackling covariate shift in RKHS-based nonparametric regressioncited by 54
  3. 2023AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptationcited by 48
  4. 2025CARROT: A Cost Aware Rate Optimal Routercited by 39
  5. 2022Adaptive and Robust Multi-Task Learningcited by 34
  6. 2022Transfer Learning for Contextual Multi-armed Banditscited by 24
  7. 2021Weighted Training for Cross-Task Learningcited by 22
  8. 2024Transfer Learning for Nonparametric Regression: Non-asymptotic Minimax Analysis and Adaptive Procedurecited by 22
  9. 2021Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approachcited by 20
  10. 2023Pseudo-Labeling for Kernel Ridge Regression under Covariate Shiftcited by 17
  11. 2024A transfer learning framework for weak-to-strong generalizationcited by 14
  12. 2022Understanding new tasks through the lens of training data via exponential tiltingcited by 11
38 more
  1. 2022A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimalitycited by 9
  2. 2023Mixture Proportion Estimation Beyond Irreducibilitycited by 9
  3. 2023Model-free Test Time Adaptation for Out-Of-Distribution Detectioncited by 9
  4. 2024Label Noise: Ignorance Is Blisscited by 9
  5. 2022Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)cited by 6
  6. 2024Transfer Learning Beyond Bounded Density Ratioscited by 6
  7. 2023Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shiftcited by 5
  8. 2024Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraintscited by 5
  9. 2024Deep Transfer Learning: Model Framework and Error Analysiscited by 5
  10. 2021Adaptive transfer learningcited by 4
  11. 2022On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Modelscited by 4
  12. 2024Help Me Help You: The Aggregate Value of Source and Target Data in Transfer Learningcited by 4
  13. 2025Test-time Correlation Alignmentcited by 4
  14. 2022Transfer Q-learningcited by 3
  15. 2023Classification Tree Pruning Under Covariate Shiftcited by 3
  16. 2023Unified Transfer Learning Models in High-Dimensional Linear Regressioncited by 3
  17. 2023Transfer Learning for Kernel-based Regressioncited by 3
  18. 2024Transfer Learning for Latent Variable Network Modelscited by 3
  19. 2024Distributionally robust risk evaluation with an isotonic constraintcited by 3
  20. 2025Conformal Prediction Under Generalized Covariate Shift with Posterior Driftcited by 3
  21. 2025Domain Generalization Under Posterior Driftcited by 3
  22. 2022On Hypothesis Transfer Learning of Functional Linear Modelscited by 2
  23. 2022Transfer learning for tensor Gaussian graphical modelscited by 2
  24. 2024When Invariant Representation Learning Meets Label Shift: Insufficiency and Theoretical Insightscited by 2
  25. 2024Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimationcited by 2
  26. 2025Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfercited by 2
  27. 2025Optimal Transfer Learning for Missing Not-at-Random Matrix Completioncited by 2
  28. 2022Automatically Score Tissue Images Like a Pathologist by Transfer Learningcited by 1
  29. 2023The ART of Transfer Learning: An Adaptive and Robust Pipelinecited by 1
  30. 2024Learning with Shared Representations: Statistical Rates and Efficient Algorithmscited by 1
  31. 2025Transfer learning via Regularized Linear Discriminant Analysiscited by 1
  32. 2025Transfer Learning through Enhanced Sufficient Representation: Enriching Source Domain Knowledge with Target Datacited by 1
  33. 2025Limitations of refinement methods for weak to strong generalizationcited by 1
  34. 2025Optimal Regularization for Performative Learningcited by 1
  35. 2026Sheaf-Laplacian Obstruction and Projection Hardness for Cross-Modal Compatibility on a Modality-Independent Sitecited by 1
  36. 2021Distributed Adaptive Nearest Neighbor Classifier: Algorithm and Theorycited by 0
  37. 2022Enhanced Nearest Neighbor Classification for Crowdsourcingcited by 0
  38. 2022A Correlation-Ratio Transfer Learning and Variational Stein's Paradoxcited by 0

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