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Algorithms and Theory for Multiple-Source Adaptation

arXiv 1805.08727 ↗First posted 20 May 2018cs.LG stat.MLcited by 53

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Abstract (arXiv)

This work includes a number of novel contributions for the multiple-source adaptation problem. We present new normalized solutions with strong theoretical guarantees for the cross-entropy loss and other similar losses. We also provide new guarantees that hold in the case where the conditional probabilities for the source domains are distinct. Moreover, we give new algorithms for determining the distribution-weighted combination solution for the cross-entropy loss and other losses. We report the results of a series of experiments with real-world datasets. We find that our algorithm outperforms competing approaches by producing a single robust model that performs well on any target mixture distribution. Altogether, our theory, algorithms, and empirical results provide a full solution for the multiple-source adaptation problem with very practical benefits.

Key facts. Cited by 53 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 May 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 20; 2022: 11; 2023: 12; 2024: 6; 2025: 2; 2026: 2.

53 papers.

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  1. 2021Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalizationcited by 248
  2. 2022Sparse Mixture-of-Experts are Domain Generalizable Learnerscited by 85
  3. 2021Unsupervised Multi-source Domain Adaptation Without Access to Source Datacited by 76
  4. 2021FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learningcited by 67
  5. 2022SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEGcited by 53
  6. 2021f-Domain-Adversarial Learning: Theory and Algorithmscited by 47
  7. 2021Online Adaptation to Label Distribution Shiftcited by 44
  8. 2021Your Classifier can Secretly Suffice Multi-Source Domain Adaptationcited by 42
  9. 2023Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learningcited by 39
  10. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  11. 2022Multi-Source Unsupervised Domain Adaptation via Pseudo Target Domaincited by 27
  12. 2023Semantic Image Segmentation: Two Decades of Researchcited by 26
38 more
  1. 2022Multi-Prompt Alignment for Multi-Source Unsupervised Domain Adaptationcited by 25
  2. 2021Aggregating From Multiple Target-Shifted Sourcescited by 24
  3. 2021T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptationcited by 24
  4. 2023Fairness and Accuracy under Domain Generalizationcited by 24
  5. 2021Multi-Source Domain Adaptation for Object Detectioncited by 21
  6. 2021Memory-aware curriculum federated learning for breast cancer classificationcited by 20
  7. 2021Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Surveycited by 19
  8. 2023Data Valuation and Detections in Federated Learningcited by 17
  9. 2022On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptationcited by 16
  10. 2021Information-theoretic regularization for Multi-source Domain Adaptationcited by 15
  11. 2021On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sourcescited by 15
  12. 2021Transferrable Contrastive Learning for Visual Domain Adaptationcited by 14
  13. 2022Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detectioncited by 14
  14. 2024LanDA: Language-Guided Multi-Source Domain Adaptationcited by 13
  15. 2024SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEGcited by 12
  16. 2024More is Better: Deep Domain Adaptation with Multiple Sourcescited by 9
  17. 2021KNOT: Knowledge Distillation using Optimal Transport for Solving NLP Taskscited by 8
  18. 2021Graphical Modeling for Multi-Source Domain Adaptationcited by 7
  19. 2021Multi-source Few-shot Domain Adaptationcited by 7
  20. 2023Best-Effort Adaptationcited by 7
  21. 2021Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classificationcited by 6
  22. 2024What Does Softmax Probability Tell Us about Classifiers Ranking Across Diverse Test Conditions?cited by 6
  23. 2023Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithmscited by 5
  24. 2023UFDA: Universal Federated Domain Adaptation with Practical Assumptionscited by 5
  25. 2024Domain adaptation in small-scale and heterogeneous biological datasetscited by 5
  26. 2022Algorithms and Theory for Supervised Gradual Domain Adaptationcited by 4
  27. 2022(Im)possibility of Collective Intelligencecited by 4
  28. 2022Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithmscited by 4
  29. 2022Gated Domain Units for Multi-source Domain Generalizationcited by 4
  30. 2026A Theoretical Framework for Modular Learning of Robust Generative Modelscited by 4
  31. 2021Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learningcited by 3
  32. 2022Union-set Multi-source Model Adaptation for Semantic Segmentationcited by 3
  33. 2023MMRDN: Consistent Representation for Multi-View Manipulation Relationship Detection in Object-Stacked Scenescited by 1
  34. 2023FREEDOM: Target Label & Source Data & Domain Information-Free Multi-Source Domain Adaptation for Unsupervised Personalizationcited by 1
  35. 2025On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspectivecited by 1
  36. 2023Local Boosting for Weakly-Supervised Learningcited by 0
  37. 2023Continual Invariant Risk Minimizationcited by 0
  38. 2023Weighted Joint Maximum Mean Discrepancy Enabled Multi-Source-Multi-Target Unsupervised Domain Adaptation Fault Diagnosiscited by 0

50 most cited of 53 All 53 (JSON)