% Key papers for Robustness-accuracy trade-off analysis (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2018_towards_28683,
  title = {{Towards Deep Learning Models Resistant to Adversarial Attacks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/67},
}

@inproceedings{icml2019_theoretically_37802,
  title = {{Theoretically Principled Trade-off between Robustness and Accuracy}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3942},
}

@inproceedings{aaai2019_interpretation_101558,
  title = {{Interpretation of Neural Networks Is Fragile}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4252},
}

@inproceedings{neurips2018_adversarially_56852,
  title = {{Adversarially Robust Generalization Requires More Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11491},
}

@inproceedings{cvpr2018_robust_13539,
  title = {{Robust Classification With Convolutional Prototype Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Yang_Robust_Classification_With_CVPR_2018_paper.html},
}

@inproceedings{icml2019_using_37842,
  title = {{Using Pre-Training Can Improve Model Robustness and Uncertainty}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4237},
}

@inproceedings{iclr2019_robustness_29103,
  title = {{Robustness May Be at Odds with Accuracy}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1032},
}

@inproceedings{iclr2019_towards_28999,
  title = {{Towards the first adversarially robust neural network model on MNIST}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1131},
}

@inproceedings{icml2019_improving_37442,
  title = {{Improving Adversarial Robustness via Promoting Ensemble Diversity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3614},
}

@inproceedings{cvpr2019_disentangling_14089,
  title = {{Disentangling Adversarial Robustness and Generalization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{aaai2020_seq2sick_101907,
  title = {{Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5767},
}

@inproceedings{neurips2019_adversarial_57882,
  title = {{Adversarial Training and Robustness for Multiple Perturbations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14536},
}

@inproceedings{icml2019_rademacher_37678,
  title = {{Rademacher Complexity for Adversarially Robust Generalization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3688},
}

@inproceedings{neurips2020_a_59233,
  title = {{A Closer Look at Accuracy vs. Robustness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17187},
}

@inproceedings{neurips2019_image_58389,
  title = {{Image Synthesis with a Single (Robust) Classifier}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13347},
}

@inproceedings{cvpr2021_adversarial_16586,
  title = {{Adversarial Robustness Under Long-Tailed Distribution}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{icml2020_attacks_37950,
  title = {{Attacks Which Do Not Kill Training Make Adversarial Learning Stronger}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5835},
}

@inproceedings{aaai2019_sensitivity_101709,
  title = {{Sensitivity Analysis of Deep Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4424},
}

@inproceedings{icml2020_confidence_38036,
  title = {{Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6559},
}

@inproceedings{iclr2019_excessive_28796,
  title = {{Excessive Invariance Causes Adversarial Vulnerability}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/900},
}

@inproceedings{cvpr2024_towards_25065,
  title = {{Towards Understanding and Improving Adversarial Robustness of Vision Transformers}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30949},
}

@inproceedings{aaai2024_adversarial_5148,
  title = {{Adversarial Purification with the Manifold Hypothesis}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29574},
}

@inproceedings{cvpr2024_towards_25039,
  title = {{Towards Fairness-Aware Adversarial Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31324},
}

@inproceedings{cvpr2024_initialization_23803,
  title = {{Initialization Matters for Adversarial Transfer Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29630},
}

@inproceedings{neurips2024_superdeepfool_73987,
  title = {{SuperDeepFool: a new fast and accurate minimal adversarial attack}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93530},
}

@inproceedings{aaai2025_aute_7991,
  title = {{AUTE: Peer-Alignment and Self-Unlearning Boost Adversarial Robustness for Training Ensemble Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32382},
}

@inproceedings{iclr2024_conserve_33678,
  title = {{Conserve-Update-Revise to Cure Generalization and Robustness Trade-off in Adversarial Training}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19397},
}

@inproceedings{iclr2024_dafa_33859,
  title = {{DAFA: Distance-Aware Fair Adversarial Training}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19216},
}

@inproceedings{iclr2024_robust_34788,
  title = {{Robust NAS under adversarial training: benchmark, theory, and beyond}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18286},
}

@inproceedings{neurips2024_detecting_72594,
  title = {{Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94783},
}

@inproceedings{neurips2024_adversarially_74389,
  title = {{Adversarially Robust Multi-task Representation Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93178},
}

@inproceedings{aaai2025_first_9200,
  title = {{First Line of Defense: A Robust First Layer Mitigates Adversarial Attacks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32771},
}

@inproceedings{iclr2024_interpreting_33988,
  title = {{Interpreting Robustness Proofs of Deep Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19087},
}

@inproceedings{icml2024_attack_43598,
  title = {{Attack-free Evaluating and Enhancing Adversarial Robustness on Categorical Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34854},
}

@inproceedings{icml2024_can_43954,
  title = {{Can Implicit Bias Imply Adversarial Robustness?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34498},
}

@inproceedings{icml2025_explaining_46617,
  title = {{Explaining the role of Intrinsic Dimensionality in Adversarial Training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45952},
}

@inproceedings{icml2025_enhancing_47177,
  title = {{Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45410},
}

@inproceedings{icml2026_improving_52980,
  title = {{Improving Adversarial Robustness of Attribution via Implicit Regularization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=aqoXbXzxlV},
}

@inproceedings{iclr2025_rethinking_81833,
  title = {{Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29949},
}

@inproceedings{iclr2025_robustness_82217,
  title = {{Robustness Reprogramming for Representation Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29574},
}