mlsanityData to 7 Oct 2026

Invariant Risk Minimization

arXiv 1907.02893 ↗First posted 5 Jul 2019stat.ML cs.AI cs.LGcited by 1,832

Not on the map. Why?

Cited by 24 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce Invariant Risk Minimization (IRM), a learning paradigm to estimate invariant correlations across multiple training distributions. To achieve this goal, IRM learns a data representation such that the optimal classifier, on top of that data representation, matches for all training distributions. Through theory and experiments, we show how the invariances learned by IRM relate to the causal structures governing the data and enable out-of-distribution generalization.

Key facts. Cited by 1,832 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jul 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 196; 2022: 368; 2023: 392; 2024: 335; 2025: 283; 2026: 257.

1,832 papers.

Sort:

By area:

  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  2. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
  3. 2021Domain Generalization: A Surveycited by 760
  4. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
  5. 2023SmoothLLM: Defending Large Language Models Against Jailbreaking Attackscited by 434
  6. 2021SWAD: Domain Generalization by Seeking Flat Minimacited by 431
  7. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  8. 2022Last Layer Re-Training is Sufficient for Robustness to Spurious Correlationscited by 379
  9. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 306
  10. 2021Gradient Matching for Domain Generalizationcited by 258
  11. 2021Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalizationcited by 220
  12. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
38 more
  1. 2021Towards Causal Representation Learningcited by 207
  2. 2022Surgical Fine-Tuning Improves Adaptation to Distribution Shiftscited by 205
  3. 2022Improving Out-of-Distribution Robustness via Selective Augmentationcited by 202
  4. 2021SelfReg: Self-supervised Contrastive Regularization for Domain Generalizationcited by 189
  5. 2021Fishr: Invariant Gradient Variances for Out-of-Distribution Generalizationcited by 186
  6. 2022Discovering Invariant Rationales for Graph Neural Networkscited by 186
  7. 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 177
  8. 2021Simple data balancing achieves competitive worst-group-accuracycited by 177
  9. 2021A Fine-Grained Analysis on Distribution Shiftcited by 171
  10. 2022Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanismcited by 171
  11. 2022Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlationscited by 171
  12. 2023Large Language Model Routing with Benchmark Datasetscited by 166
  13. 2021Scene Transformer: A unified architecture for predicting multiple agent trajectoriescited by 165
  14. 2021A Survey of Zero-shot Generalisation in Deep Reinforcement Learningcited by 164
  15. 2022Handling Distribution Shifts on Graphs: An Invariance Perspectivecited by 163
  16. 2021Deep Stable Learning for Out-Of-Distribution Generalizationcited by 153
  17. 2022Diverse Weight Averaging for Out-of-Distribution Generalizationcited by 148
  18. 2022On Feature Learning in the Presence of Spurious Correlationscited by 145
  19. 2024WARM: On the Benefits of Weight Averaged Reward Modelscited by 145
  20. 2021Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalizationcited by 144
  21. 2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144
  22. 2022GOOD: A Graph Out-of-Distribution Benchmarkcited by 140
  23. 2022Causality Inspired Representation Learning for Domain Generalizationcited by 136
  24. 2021On Calibration and Out-of-domain Generalizationcited by 134
  25. 2022Domain Generalization by Mutual-Information Regularization with Pre-trained Modelscited by 133
  26. 2021Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learningcited by 123
  27. 2021Goal Misgeneralization in Deep Reinforcement Learningcited by 122
  28. 2021MSeg: A Composite Dataset for Multi-domain Semantic Segmentationcited by 122
  29. 2023Change is Hard: A Closer Look at Subpopulation Shiftcited by 119
  30. 2021Causality-inspired Single-source Domain Generalization for Medical Image Segmentationcited by 118
  31. 2021Does Invariant Risk Minimization Capture Invariance?cited by 116
  32. 2021Counterfactual Generative Networkscited by 111
  33. 2022Disentangled Representation Learningcited by 111
  34. 2021Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networkscited by 108
  35. 2021Causal Attention for Interpretable and Generalizable Graph Classificationcited by 108
  36. 2021Measure and Improve Robustness in NLP Models: A Surveycited by 107
  37. 2024Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factorscited by 106
  38. 2023Nonparametric Identifiability of Causal Representations from Unknown Interventionscited by 105

50 most cited of 1,832 All 1,832 (JSON)