mlsanityData to 14 Oct 2026

A Generalization Error Bound for Multi-class Domain Generalization

arXiv 1905.10392 ↗First posted 24 May 2019stat.ML cs.LGcited by 26

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Abstract (arXiv)

Domain generalization is the problem of assigning labels to an unlabeled data set, given several similar data sets for which labels have been provided. Despite considerable interest in this problem over the last decade, there has been no theoretical analysis in the setting of multi-class classification. In this work, we study a kernel-based learning algorithm and establish a generalization error bound that scales logarithmically in the number of classes, matching state-of-the-art bounds for multi-class classification in the conventional learning setting. We also demonstrate empirically that the proposed algorithm achieves significant performance gains compared to a pooling strategy.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 May 2019.

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Citing papers per year: 2021: 13; 2022: 6; 2023: 4; 2024: 1; 2025: 2.

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  1. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 766
  2. 2021Domain Generalization: A Surveycited by 764
  3. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 436
  4. 2021Model-Based Domain Generalizationcited by 104
  5. 2021Towards a Theoretical Framework of Out-of-Distribution Generalizationcited by 94
  6. 2022Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalizationcited by 69
  7. 2021Adaptive Methods for Real-World Domain Generalizationcited by 66
  8. 2022Probable Domain Generalization via Quantile Risk Minimizationcited by 66
  9. 2024Credal Learning Theorycited by 31
  10. 2021An Online Learning Approach to Interpolation and Extrapolation in Domain Generalizationcited by 30
  11. 2023Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalizationcited by 29
  12. 2023Fairness and Accuracy under Domain Generalizationcited by 24
14 more
  1. 2023MADG: Margin-based Adversarial Learning for Domain Generalizationcited by 22
  2. 2021On the benefits of representation regularization in invariance based domain generalizationcited by 17
  3. 2021On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sourcescited by 15
  4. 2023Graph Structure and Feature Extrapolation for Out-of-Distribution Generalizationcited by 10
  5. 2022HMOE: Hypernetwork-based Mixture of Experts for Domain Generalizationcited by 8
  6. 2025Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'cited by 8
  7. 2021Fine-grained Generalization Analysis of Vector-valued Learningcited by 5
  8. 2022Generalization Properties of Retrieval-based Modelscited by 5
  9. 2021Discriminative Domain-Invariant Adversarial Network for Deep Domain Generalizationcited by 4
  10. 2021Explainability-aided Domain Generalization for Image Classificationcited by 1
  11. 2021Offline RL With Resource Constrained Online Deploymentcited by 1
  12. 2025Epistemic Errors of Imperfect Multitask Learners When Distributions Shiftcited by 1
  13. 2022Counterfactual Supervision-based Information Bottleneck for Out-of-Distribution Generalizationcited by 0
  14. 2022Structural State Translation: Condition Transfer between Civil Structures Using Domain-Generalization for Structural Health Monitoringcited by 0