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Theoretical Analysis of Domain Adaptation with Optimal Transport

arXiv 1610.04420 ↗First posted 14 Oct 2016stat.ML cs.LGcited by 105

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Abstract (arXiv)

Domain adaptation (DA) is an important and emerging field of machine learning that tackles the problem occurring when the distributions of training (source domain) and test (target domain) data are similar but different. Current theoretical results show that the efficiency of DA algorithms depends on their capacity of minimizing the divergence between source and target probability distributions. In this paper, we provide a theoretical study on the advantages that concepts borrowed from optimal transportation theory can bring to DA. In particular, we show that the Wasserstein metric can be used as a divergence measure between distributions to obtain generalization guarantees for three different learning settings: (i) classic DA with unsupervised target data (ii) DA combining source and target labeled data, (iii) multiple source DA. Based on the obtained results, we provide some insights showing when this analysis can be tighter than other existing frameworks.

Key facts. Cited by 105 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Oct 2016.

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Citing papers per year: 2021: 19; 2022: 18; 2023: 20; 2024: 17; 2025: 15; 2026: 16.

105 papers.

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  1. 2022Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptationcited by 66
  2. 2023Recent Advances in Optimal Transport for Machine Learningcited by 66
  3. 2021Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improvecited by 46
  4. 2021Meta-Learning Dynamics Forecasting Using Task Inferencecited by 37
  5. 2021A Survey on Optimal Transport for Machine Learning: Theory and Applicationscited by 35
  6. 2023Optimal Transport for Treatment Effect Estimationcited by 32
  7. 2023Scalable Optimal Transport Methods in Machine Learning: A Contemporary Surveycited by 27
  8. 2023Semi-Supervised Domain Adaptation with Source Label Adaptationcited by 26
  9. 2021Assessing Fairness in the Presence of Missing Datacited by 24
  10. 2021Cross-domain Activity Recognition via Substructural Optimal Transportcited by 21
  11. 2022An improved central limit theorem and fast convergence rates for entropic transportation costscited by 18
  12. 2023Data Valuation and Detections in Federated Learningcited by 17
38 more
  1. 2021Cognitively Inspired Learning of Incremental Drifting Conceptscited by 16
  2. 2021Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transportcited by 16
  3. 2021On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sourcescited by 15
  4. 2022PAC-Bayesian Domain Adaptation Bounds for Multiclass Learnerscited by 13
  5. 2022Unsupervised Model Adaptation for Source-free Segmentation of Medical Imagescited by 13
  6. 2021Secure Domain Adaptation with Multiple Sourcescited by 12
  7. 2022The Value of Out-of-Distribution Datacited by 12
  8. 2022Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Surveycited by 12
  9. 2023Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Spacecited by 12
  10. 2022Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networkscited by 10
  11. 2022Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transportcited by 9
  12. 2024Accelerating Sinkhorn Algorithm with Sparse Newton Iterationscited by 9
  13. 2024Can Modifying Data Address Graph Domain Adaptation?cited by 9
  14. 2025Generalizable Domain Adaptation for Sim-and-Real Policy Co-Trainingcited by 9
  15. 2023Best-Effort Adaptationcited by 7
  16. 2021Learning Domain Invariant Representations by Joint Wasserstein Distance Minimizationcited by 6
  17. 2024Bridging the Domain Gap for Flight-Ready Spaceborne Visioncited by 6
  18. 2025Domain Adaptation and Entanglement: an Optimal Transport Perspectivecited by 6
  19. 2021Multi-task Learning by Leveraging the Semantic Informationcited by 5
  20. 2024Degree-Conscious Spiking Graph for Cross-Domain Adaptationcited by 5
  21. 2021Cross-hospital Sepsis Early Detection via Semi-supervised Optimal Transport with Self-paced Ensemblecited by 4
  22. 2021Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separationcited by 4
  23. 2022Gap Minimization for Knowledge Sharing and Transfercited by 4
  24. 2022LEATHER: A Framework for Learning to Generate Human-like Text in Dialoguecited by 4
  25. 2024Optimal Aggregation of Prediction Intervals under Unsupervised Domain Shiftcited by 4
  26. 2025A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classescited by 4
  27. 2021A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithmscited by 3
  28. 2021Label Propagation Through Optimal Transportcited by 3
  29. 2022Modeling Hierarchical Structural Distance for Unsupervised Domain Adaptationcited by 3
  30. 2023Preserving Fairness in AI under Domain Shiftcited by 3
  31. 2023Out-Of-Domain Unlabeled Data Improves Generalizationcited by 3
  32. 2023Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmarkcited by 3
  33. 2024A Sinkhorn-type Algorithm for Constrained Optimal Transportcited by 3
  34. 2024Optimal Transport for Domain Adaptation through Gaussian Mixture Modelscited by 3
  35. 2024Help Me Help You: The Aggregate Value of Source and Target Data in Transfer Learningcited by 3
  36. 2024In-Context Transfer Learning: Demonstration Synthesis by Transferring Similar Taskscited by 3
  37. 2026A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't)cited by 3
  38. 2021Affine Transport for Sim-to-Real Domain Adaptationcited by 2

50 most cited of 105 All 105 (JSON)