% Key papers for Theory of adversarial risk (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2020_measuring_60296,
  title = {{Measuring Robustness to Natural Distribution Shifts in Image Classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17118},
}

@inproceedings{cvpr2022_pixmix_19582,
  title = {{PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{aaai2019_the_101661,
  title = {{The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4373},
}

@inproceedings{neurips2019_robustness_58925,
  title = {{Robustness to Adversarial Perturbations in Learning from Incomplete Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13682},
}

@inproceedings{aaai2019_verifying_101216,
  title = {{Verifying Robustness of Gradient Boosted Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4089},
}

@inproceedings{icml2019_generalized_37394,
  title = {{Generalized No Free Lunch Theorem for Adversarial Robustness}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4225},
}

@inproceedings{neurips2020_boundary_59482,
  title = {{Boundary thickness and robustness in learning models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18236},
}

@inproceedings{iclr2020_understanding_29761,
  title = {{Understanding the Limitations of Conditional Generative Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=r1lPleBFvH},
}

@inproceedings{aaai2019_towards_101550,
  title = {{Towards Reliable Learning for High Stakes Applications}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4242},
}

@inproceedings{cvpr2023_seasoning_22141,
  title = {{Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21052},
}

@inproceedings{icml2020_efficiently_38153,
  title = {{Efficiently Learning Adversarially Robust Halfspaces with Noise}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6754},
}

@inproceedings{neurips2018_adversarial_56848,
  title = {{Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11980},
}

@inproceedings{icml2020_sharp_38758,
  title = {{Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6312},
}

@inproceedings{icml2022_on_40928,
  title = {{On the Adversarial Robustness of Causal Algorithmic Recourse}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/18199},
}

@inproceedings{icml2021_evaluating_39307,
  title = {{Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10405},
}

@inproceedings{neurips2020_a_59251,
  title = {{A General Method for Robust Learning from Batches}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18393},
}

@inproceedings{cvpr2022_enhancing_18765,
  title = {{Enhancing Classifier Conservativeness and Robustness by Polynomiality}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{neurips2020_provably_60641,
  title = {{Provably Robust Metric Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17517},
}

@inproceedings{neurips2021_adversarially_62368,
  title = {{Adversarially robust learning for security-constrained optimal power flow}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27413},
}

@inproceedings{neurips2021_on_62939,
  title = {{On the Existence of The Adversarial Bayes Classifier}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28566},
}

@inproceedings{neurips2024_collaboration_72706,
  title = {{Collaboration! Towards Robust Neural Methods for Routing Problems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94681},
}

@inproceedings{aaai2025_on_10184,
  title = {{On the Robustness of Distributed Machine Learning Against Transfer Attacks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33688},
}

@inproceedings{icml2024_geometry_43522,
  title = {{Geometry-Aware Instrumental Variable Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34930},
}

@inproceedings{icml2025_sgd_49122,
  title = {{SGD Jittering: A Training Strategy for Robust and Accurate Model-Based Architectures}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43503},
}

@inproceedings{icml2026_adversarially_50600,
  title = {{Adversarially Robust Approximate Furthest Neighbor}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=E0wwKfaTE0},
}

@inproceedings{icml2026_calibrating_53135,
  title = {{Calibrating Uncertainty for Zero-Shot Adversarial CLIP}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=cQRkHJGgj1},
}

@inproceedings{neurips2024_on_70540,
  title = {{On the Adversarial Robustness of Benjamini Hochberg}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96573},
}

@inproceedings{neurips2024_the_73346,
  title = {{The Price of Implicit Bias in Adversarially Robust Generalization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94100},
}

@inproceedings{iclr2025_robust_82622,
  title = {{Robust Feature Learning for Multi-Index Models in High Dimensions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29175},
}

@inproceedings{iclr2025_provable_83723,
  title = {{Provable Robust Overfitting Mitigation in Wasserstein Distributionally Robust Optimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28096},
}

@inproceedings{iclr2026_robust_84384,
  title = {{Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=27oJibuygA},
}

@inproceedings{iclr2026_robust_87322,
  title = {{Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=aIBFTh2ThF},
}

@inproceedings{iclr2026_noise_88436,
  title = {{Noise Tolerance of Distributionally Robust Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=mf35JXqWHS},
}

@inproceedings{neurips2026_benchmarking_92352,
  title = {{Benchmarking Sensor-Fault Robustness in Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/138912},
}

@inproceedings{icml2024_consistent_44216,
  title = {{Consistent Adversarially Robust Linear Classification: Non-Parametric Setting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34236},
}

@inproceedings{icml2025_optimal_47409,
  title = {{Optimal Fair Learning Robust to Adversarial Distribution Shift}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45184},
}

@inproceedings{icml2025_gradient_49070,
  title = {{Gradient Flow Provably Learns Robust Classifiers for Orthonormal GMMs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43554},
}

@inproceedings{iclr2025_evidential_80753,
  title = {{Evidential Learning-based Certainty Estimation for Robust Dense Feature Matching}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/31015},
}

@inproceedings{iclr2026_a_86046,
  title = {{A Unified Total Variation Framework for Membrane Potential Perturbation Dynamic}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=LDo9numrx6},
}

@inproceedings{iclr2026_ferd_88114,
  title = {{FERD: Fairness-Enhanced Data-Free Adversarial Robustness Distillation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=jGXTx64gal},
}