% Key papers for Out-of-distribution generalization theory (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2022_ood_19515,
  title = {{OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{aaai2021_decaug_365,
  title = {{DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/06705-decaug-out-of-distribution-generalization-via-decomposed-feature-representation-and-semantic-augmentation/},
}

@inproceedings{neurips2021_learning_61150,
  title = {{Learning Causal Semantic Representation for Out-of-Distribution Prediction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27172},
}

@inproceedings{neurips2021_invariance_62999,
  title = {{Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26541},
}

@inproceedings{cvpr2022_out_19531,
  title = {{Out-of-Distribution Generalization With Causal Invariant Transformations}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{neurips2021_towards_61181,
  title = {{Towards a Theoretical Framework of Out-of-Distribution Generalization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27952},
}

@inproceedings{cvpr2022_evading_18781,
  title = {{Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions With Superior OOD Generalization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{icml2021_accuracy_39011,
  title = {{Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9243},
}

@inproceedings{cvpr2024_can_22945,
  title = {{Can Biases in ImageNet Models Explain Generalization?}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30844},
}

@inproceedings{icml2021_can_39124,
  title = {{Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9481},
}

@inproceedings{cvpr2024_rethinking_24639,
  title = {{Rethinking the Evaluation Protocol of Domain Generalization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31636},
}

@inproceedings{iclr2023_harnessing_32856,
  title = {{Harnessing Out-Of-Distribution Examples via Augmenting Content and Style}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/12215},
}

@inproceedings{iclr2023_pareto_32930,
  title = {{Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/10774},
}

@inproceedings{iclr2021_in_30575,
  title = {{In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2947},
}

@inproceedings{aaai2023_on_4283,
  title = {{On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26250},
}

@inproceedings{icml2023_feed_41522,
  title = {{Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24661},
}

@inproceedings{neurips2022_using_63991,
  title = {{Using Mixup as a Regularizer Can Surprisingly Improve Accuracy \& Out-of-Distribution Robustness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53348},
}

@inproceedings{neurips2023_id_67585,
  title = {{ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72156},
}

@inproceedings{neurips2023_invariant_67723,
  title = {{Invariant Learning via Probability of Sufficient and Necessary Causes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72030},
}

@inproceedings{aaai2023_certifiable_3250,
  title = {{Certifiable Out-of-Distribution Generalization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26295},
}

@inproceedings{aaai2024_diagnosing_5684,
  title = {{Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approach}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29028},
}

@inproceedings{aaai2024_maxent_6611,
  title = {{MaxEnt Loss: Constrained Maximum Entropy for Calibration under Out-of-Distribution Shift}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30143},
}

@inproceedings{aaai2024_retroood_7142,
  title = {{RetroOOD: Understanding Out-of-Distribution Generalization in Retrosynthesis Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27791},
}

@inproceedings{iclr2024_neuron_34468,
  title = {{Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18606},
}

@inproceedings{aaai2024_coverage_5521,
  title = {{Coverage-Guaranteed Prediction Sets for Out-of-Distribution Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29673},
}

@inproceedings{iclr2024_spurious_34799,
  title = {{Spurious Feature Diversification Improves Out-of-distribution Generalization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18275},
}

@inproceedings{iclr2025_in_81446,
  title = {{In Search of Forgotten Domain Generalization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30328},
}

@inproceedings{iclr2024_unraveling_34231,
  title = {{Unraveling the Key Components of OOD Generalization via Diversification}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18844},
}

@inproceedings{iclr2024_hypo_34573,
  title = {{HYPO: Hyperspherical Out-Of-Distribution Generalization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18501},
}

@inproceedings{icml2024_domain_45775,
  title = {{Domain Generalisation via Imprecise Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32677},
}

@inproceedings{neurips2024_aha_70414,
  title = {{AHA: Human-Assisted Out-of-Distribution Generalization and Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96691},
}

@inproceedings{neurips2024_what_73955,
  title = {{What Variables Affect Out-of-Distribution Generalization in Pretrained Models?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93557},
}

@inproceedings{neurips2025_rethinking_77562,
  title = {{Rethinking Out-of-Distribution Detection and Generalization with Collective Behavior Dynamics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=VD22PY0fZm},
}

@inproceedings{iclr2024_towards_35456,
  title = {{Towards Robust Out-of-Distribution Generalization Bounds via Sharpness}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17617},
}

@inproceedings{icml2024_discovering_45022,
  title = {{Discovering Environments with XRM}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33430},
}

@inproceedings{icml2024_split_45410,
  title = {{Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33041},
}

@inproceedings{icml2025_ood_45881,
  title = {{OOD-Chameleon: Is Algorithm Selection for OOD Generalization Learnable?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46662},
}

@inproceedings{icml2025_controlling_46273,
  title = {{Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46287},
}