mlsanityData to 7 Oct 2026

Hypothesis Transfer Learning via Transformation Functions

arXiv 1612.01020 ↗First posted 3 Dec 2016stat.ML cs.LGcited by 26

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Abstract (arXiv)

We consider the Hypothesis Transfer Learning (HTL) problem where one incorporates a hypothesis trained on the source domain into the learning procedure of the target domain. Existing theoretical analysis either only studies specific algorithms or only presents upper bounds on the generalization error but not on the excess risk. In this paper, we propose a unified algorithm-dependent framework for HTL through a novel notion of transformation function, which characterizes the relation between the source and the target domains. We conduct a general risk analysis of this framework and in particular, we show for the first time, if two domains are related, HTL enjoys faster convergence rates of excess risks for Kernel Smoothing and Kernel Ridge Regression than those of the classical non-transfer learning settings. Experiments on real world data demonstrate the effectiveness of our framework.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Dec 2016.

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Citing papers per year: 2021: 4; 2022: 8; 2023: 5; 2024: 3; 2025: 5; 2026: 1.

26 papers.

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  1. 2022Knowledge Distillation with the Reused Teacher Classifiercited by 140
  2. 2022Deep transfer operator learning for partial differential equations under conditional shiftcited by 83
  3. 2021On Inductive Biases for Heterogeneous Treatment Effect Estimationcited by 74
  4. 2021On the Power of Multitask Representation Learning in Linear MDPcited by 30
  5. 2021Representation Learning Beyond Linear Prediction Functionscited by 21
  6. 2021A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?cited by 14
  7. 2022Generalized Knowledge Distillation via Relationship Matchingcited by 12
  8. 2022Provable General Function Class Representation Learning in Multitask Bandits and MDPscited by 12
  9. 2024TransFusion: Covariate-Shift Robust Transfer Learning for High-Dimensional Regressioncited by 11
  10. 2025Task Generalization With AutoRegressive Compositional Structure: Can Learning From D Tasks Generalize to DT Tasks?cited by 11
  11. 2024Towards Robust Out-of-Distribution Generalization Bounds via Sharpnesscited by 10
  12. 2024Smoothness Adaptive Hypothesis Transfer Learningcited by 8
14 more
  1. 2023Best-Effort Adaptationcited by 7
  2. 2023Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stabilitycited by 4
  3. 2023Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptationcited by 4
  4. 2022Transfer learning with affine model transformationcited by 3
  5. 2022On Hypothesis Transfer Learning of Functional Linear Modelscited by 2
  6. 2022Hypothesis Transfer in Bandits by Weighted Modelscited by 2
  7. 2023A Prototype-Oriented Clustering for Domain Shift with Source Privacycited by 2
  8. 2025Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfercited by 2
  9. 2025Towards Understanding the Benefit of Multitask Representation Learning in Decision Processcited by 1
  10. 2022ISTRBoost: Importance Sampling Transfer Regression using Boostingcited by 0
  11. 2023Source-free Domain Adaptation Requires Penalized Diversitycited by 0
  12. 2025Transfer Learning of Surrogate Models: Integrating Domain Warping and Affine Transformationscited by 0
  13. 2025Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learningcited by 0
  14. 2026Transfer Learning in Nonparametric Regression with Deep ReLU Networkscited by 0