mlsanityData to 7 Oct 2026

A Theory of Multiple-Source Adaptation with Limited Target Labeled Data

arXiv 2007.09762 ↗First posted 19 Jul 2020cs.LG stat.MLcited by 18

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Abstract (arXiv)

We present a theoretical and algorithmic study of the multiple-source domain adaptation problem in the common scenario where the learner has access only to a limited amount of labeled target data, but where the learner has at disposal a large amount of labeled data from multiple source domains. We show that a new family of algorithms based on model selection ideas benefits from very favorable guarantees in this scenario and discuss some theoretical obstacles affecting some alternative techniques. We also report the results of several experiments with our algorithms that demonstrate their practical effectiveness.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jul 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 5; 2023: 5; 2024: 3; 2025: 0; 2026: 1.

18 papers.

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  1. 2021Aggregating From Multiple Target-Shifted Sourcescited by 24
  2. 2023Data Valuation and Detections in Federated Learningcited by 17
  3. 2021On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sourcescited by 15
  4. 2021Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithmcited by 14
  5. 2021Communication-Efficient Agnostic Federated Averagingcited by 10
  6. 2022On the Generalization of Wasserstein Robust Federated Learningcited by 9
  7. 2023Best-Effort Adaptationcited by 7
  8. 2022Gap Minimization for Knowledge Sharing and Transfercited by 4
  9. 2022Algorithms and Theory for Supervised Gradual Domain Adaptationcited by 4
  10. 2023Probably Approximately Correct Federated Learningcited by 4
  11. 2022Wasserstein Distributionally Robust Optimization with Wasserstein Barycenterscited by 3
  12. 2024Robust Offline Policy Learning with Observational Data from Multiple Sourcescited by 3
6 more
  1. 2024Collaborative Learning with Different Labeling Functionscited by 2
  2. 2022PAC Generalization via Invariant Representationscited by 1
  3. 2023Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptationcited by 1
  4. 2023Federated Offline Policy Learningcited by 0
  5. 2024Collaborative Multi-source Domain Adaptation Through Optimal Transportcited by 0
  6. 2026Guided Uncertainty-Aware Robust Domain Transfercited by 0