mlsanityData to 14 Oct 2026

Stable Learning via Sample Reweighting

arXiv 1911.12580 ↗First posted 28 Nov 2019cs.LG stat.MLcited by 70

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Abstract (arXiv)

We consider the problem of learning linear prediction models with model misspecification bias. In such case, the collinearity among input variables may inflate the error of parameter estimation, resulting in instability of prediction results when training and test distributions do not match. In this paper we theoretically analyze this fundamental problem and propose a sample reweighting method that reduces collinearity among input variables. Our method can be seen as a pretreatment of data to improve the condition of design matrix, and it can then be combined with any standard learning method for parameter estimation and variable selection. Empirical studies on both simulation and real datasets demonstrate the effectiveness of our method in terms of more stable performance across different distributed data.

Key facts. Cited by 70 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Nov 2019.

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Citing papers per year: 2021: 8; 2022: 18; 2023: 18; 2024: 5; 2025: 14; 2026: 7.

70 papers.

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  1. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 436
  2. 2021Deep Stable Learning for Out-Of-Distribution Generalizationcited by 153
  3. 2021OOD-GNN: Out-of-Distribution Generalized Graph Neural Networkcited by 78
  4. 2021Generalizing Graph Neural Networks on Out-Of-Distribution Graphscited by 71
  5. 2022Out-of-distribution Generalization with Causal Invariant Transformationscited by 45
  6. 2023MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoningcited by 45
  7. 2023Model Agnostic Sample Reweighting for Out-of-Distribution Learningcited by 44
  8. 2023Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalizationcited by 29
  9. 2022Debiased Graph Neural Networks with Agnostic Label Selection Biascited by 25
  10. 2024A Survey on Evaluation of Out-of-Distribution Generalizationcited by 21
  11. 2023Rethinking the Evaluation Protocol of Domain Generalizationcited by 20
  12. 2021A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalizationcited by 19
38 more
  1. 2022Exploring Optimal Substructure for Out-of-distribution Generalization via Feature-targeted Model Pruningcited by 17
  2. 2022Towards Domain Generalization in Object Detectioncited by 16
  3. 2022Label-Efficient Domain Generalization via Collaborative Exploration and Generalizationcited by 15
  4. 2021Distributionally Robust Learning with Stable Adversarial Trainingcited by 14
  5. 2022Stable Learning via Sparse Variable Independencecited by 13
  6. 2022ZooD: Exploiting Model Zoo for Out-of-Distribution Generalizationcited by 12
  7. 2022Instrumental Variables in Causal Inference and Machine Learning: A Surveycited by 10
  8. 2023ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representationscited by 10
  9. 2023Graph Structure and Feature Extrapolation for Out-of-Distribution Generalizationcited by 10
  10. 2023SFP: Spurious Feature-targeted Pruning for Out-of-Distribution Generalizationcited by 9
  11. 2023Grounded Image Text Matching with Mismatched Relation Reasoningcited by 8
  12. 2021General Greedy De-bias Learningcited by 7
  13. 2021Understanding and Testing Generalization of Deep Networks on Out-of-Distribution Datacited by 5
  14. 2022Towards Class-agnostic Tracking Using Feature Decorrelation in Point Cloudscited by 5
  15. 2022RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoringcited by 5
  16. 2022PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognitioncited by 5
  17. 2023Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendationcited by 5
  18. 2022Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspectivecited by 4
  19. 2022Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithmscited by 4
  20. 2023Quantifying Overfitting: Evaluating Neural Network Performance through Analysis of Null Spacecited by 4
  21. 2023Learning Generalizable Agents via Saliency-Guided Features Decorrelationcited by 4
  22. 2025Distribution Shift Aware Neural Tabular Learningcited by 4
  23. 2022Deep Stable Representation Learning on Electronic Health Recordscited by 3
  24. 2023Who Would be Interested in Services? An Entity Graph Learning System for User Targetingcited by 3
  25. 2025Causal invariant geographic network representations with feature and structural distribution shiftscited by 3
  26. 2022Time Will Change Things: An Empirical Study on Dynamic Language Understanding in Social Media Classificationcited by 2
  27. 2023Deep Stable Multi-Interest Learning for Out-of-distribution Sequential Recommendationcited by 2
  28. 2025Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergiescited by 2
  29. 2022EiHi Net: Out-of-Distribution Generalization Paradigmcited by 1
  30. 2023Invariant Random Forest: Tree-Based Model Solution for OOD Generalizationcited by 1
  31. 2024Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populationscited by 1
  32. 2024Establishing Causal Relationship Between Whole Slide Image Predictions and Diagnostic Evidence Subregions in Deep Learningcited by 1
  33. 2025Bayesian Cross-Modal Alignment Learning for Few-Shot Out-of-Distribution Generalizationcited by 1
  34. 2025Class-Conditional Distribution Balancing for Group Robust Classificationcited by 1
  35. 2025Stability Selection via Variable Decorrelationcited by 1
  36. 2025Decorrelated feature importance from local sample weightingcited by 1
  37. 2022Improving Multi-Interest Network with Stable Learningcited by 0
  38. 2022A Novel Plug-and-Play Approach for Adversarially Robust Generalizationcited by 0

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