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

@inproceedings{iclr2020_augmix_29593,
  title = {{AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=S1gmrxHFvB},
}

@inproceedings{icml2021_wilds_40107,
  title = {{WILDS: A Benchmark of in-the-Wild Distribution Shifts}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10117},
}

@inproceedings{icml2020_test_38837,
  title = {{Test-Time Training with Self-Supervision for Generalization under Distribution Shifts}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6552},
}

@inproceedings{aaai2022_ddg_1911,
  title = {{DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/04092-ddg-da-data-distribution-generation-for-predictable-concept-drift-adaptation/},
}

@inproceedings{iclr2021_understanding_30514,
  title = {{Understanding the failure modes of out-of-distribution generalization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2659},
}

@inproceedings{icml2020_the_38843,
  title = {{The Effect of Natural Distribution Shift on Question Answering Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6341},
}

@inproceedings{iclr2021_breeds_30614,
  title = {{BREEDS: Benchmarks for Subpopulation Shift}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3265},
}

@inproceedings{neurips2022_probable_64012,
  title = {{Probable Domain Generalization via Quantile Risk Minimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53949},
}

@inproceedings{cvpr2024_any_22774,
  title = {{Any-Shift Prompting for Generalization over Distributions}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30450},
}

@inproceedings{iclr2024_on_35290,
  title = {{On the Variance of Neural Network Training with respect to Test Sets and Distributions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17783},
}

@inproceedings{iclr2022_visual_30943,
  title = {{Visual Representation Learning Does Not Generalize Strongly Within the Same Domain}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6276},
}

@inproceedings{neurips2021_training_62326,
  title = {{Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27251},
}

@inproceedings{neurips2024_a_73604,
  title = {{A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97534},
}

@inproceedings{neurips2024_emgbench_74144,
  title = {{EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97478},
}

@inproceedings{neurips2021_cockpit_61454,
  title = {{Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27382},
}

@inproceedings{neurips2023_neural_67324,
  title = {{Neural Priming for Sample-Efficient Adaptation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72401},
}

@inproceedings{neurips2023_coda_67715,
  title = {{CODA: Generalizing to Open and Unseen Domains with Compaction and Disambiguation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72038},
}

@inproceedings{neurips2024_touchstone_72731,
  title = {{Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97634},
}

@inproceedings{icml2025_watch_46740,
  title = {{WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45832},
}

@inproceedings{neurips2021_characterizing_62577,
  title = {{Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28292},
}

@inproceedings{icml2026_mitigating_50031,
  title = {{Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=8N2kUoknvy},
}

@inproceedings{neurips2024_online_71411,
  title = {{Online Feature Updates Improve Online (Generalized) Label Shift Adaptation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95841},
}

@inproceedings{neurips2024_revealing_72951,
  title = {{Revealing Distribution Discrepancy by Sampling Transfer in Unlabeled Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94461},
}

@inproceedings{iclr2025_addressing_83252,
  title = {{Addressing Label Shift in Distributed Learning via Entropy Regularization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28558},
}

@inproceedings{icml2024_domain_43264,
  title = {{Domain-wise Data Acquisition to Improve Performance under Distribution Shift}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35188},
}

@inproceedings{iclr2026_temporal_87046,
  title = {{Temporal Generalization: A Reality Check}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=Wz0ILlbh9U},
}