% Key papers for Robust overfitting and regularization (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2018_a_56810,
  title = {{A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11690},
}

@inproceedings{icml2017_a_36032,
  title = {{A Closer Look at Memorization in Deep Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/874},
}

@inproceedings{cvpr2021_natural_17469,
  title = {{Natural Adversarial Examples}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2020_high_15650,
  title = {{High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{icml2019_manifold_37532,
  title = {{Manifold Mixup: Better Representations by Interpolating Hidden States}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3776},
}

@inproceedings{iclr2017_deep_28291,
  title = {{Deep Variational Information Bottleneck}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=HyxQzBceg},
}

@inproceedings{icml2019_learning_37510,
  title = {{Learning with Bad Training Data via Iterative Trimmed Loss Minimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4165},
}

@inproceedings{iclr2020_rethinking_29370,
  title = {{Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=Byg9A24tvB},
}

@inproceedings{aaai2018_regularizing_102421,
  title = {{Regularizing Deep Networks Using Efficient Layerwise Adversarial Training}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11688},
}

@inproceedings{icml2019_interpreting_37454,
  title = {{Interpreting Adversarially Trained Convolutional Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3778},
}

@inproceedings{neurips2020_maximum_60295,
  title = {{Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16807},
}

@inproceedings{cvpr2018_wasserstein_13718,
  title = {{Wasserstein Introspective Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Lee_Wasserstein_Introspective_Neural_CVPR_2018_paper.html},
}

@inproceedings{aaai2020_invariant_101988,
  title = {{Invariant Representations through Adversarial Forgetting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5850},
}

@inproceedings{cvpr2022_improving_19060,
  title = {{Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{iclr2021_how_30057,
  title = {{How Does Mixup Help With Robustness and Generalization?}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2734},
}

@inproceedings{aaai2021_simple_1387,
  title = {{Simple and Effective Stochastic Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/03252-simple-and-effective-stochastic-neural-networks/},
}

@inproceedings{iclr2021_on_30624,
  title = {{On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2906},
}

@inproceedings{neurips2019_resnets_58906,
  title = {{ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13375},
}

@inproceedings{iclr2022_exploring_30917,
  title = {{Exploring Memorization in Adversarial Training}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6359},
}

@inproceedings{neurips2019_invariance_58432,
  title = {{Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14388},
}

@inproceedings{cvpr2024_boosting_22859,
  title = {{Boosting Adversarial Training via Fisher-Rao Norm-based Regularization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29323},
}

@inproceedings{cvpr2024_focus_23494,
  title = {{Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial Training}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30466},
}

@inproceedings{aaai2025_sap_10591,
  title = {{SAP: Corrective Machine Unlearning with Scaled Activation Projection for Label Noise Robustness}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33972},
}

@inproceedings{cvpr2025_gain_26378,
  title = {{Gain from Neighbors: Boosting Model Robustness in the Wild via Adversarial Perturbations Toward Neighboring Classes}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33165},
}

@inproceedings{iclr2024_on_33528,
  title = {{On the Over-Memorization During Natural, Robust and Catastrophic Overfitting}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19547},
}

@inproceedings{iclr2024_the_35030,
  title = {{The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18044},
}

@inproceedings{iclr2024_theoretical_33505,
  title = {{Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19570},
}

@inproceedings{aaai2026_mpd_100115,
  title = {{MPD-SGR: Robust Spiking Neural Networks with Membrane Potential Distribution-Driven Surrogate Gradient Regularization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37166},
}

@inproceedings{icml2025_on_48433,
  title = {{On the Clean Generalization and Robust Overfitting in Adversarial Training from Two Theoretical Views: Representation Complexity and Training Dynamics}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44184},
}

@inproceedings{icml2026_improving_52221,
  title = {{Improving ML Attacks on LWE with Data Repetition and Stepwise Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Tq5dFAGeBL},
}

@inproceedings{neurips2025_dynamical_75353,
  title = {{Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=7AwFJzgIUW},
}

@inproceedings{neurips2025_adversarial_77668,
  title = {{Adversarial generalization of unfolding (model-based) networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=WF9AyBTIjw},
}

@inproceedings{neurips2025_distributional_80008,
  title = {{Distributional Adversarial Attacks and Training in Deep Hedging}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=vBtfIafffU},
}

@inproceedings{iclr2025_learning_82320,
  title = {{Learning Randomized Algorithms with Transformers}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29471},
}

@inproceedings{iclr2025_enhancing_83298,
  title = {{Enhancing Robust Fairness via Confusional Spectral Regularization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28515},
}

@inproceedings{iclr2026_exposing_84974,
  title = {{Exposing and Defending the Achilles' Heel of Video Mixture-of-Experts}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=8voly42rKo},
}

@inproceedings{iclr2026_understanding_88265,
  title = {{Understanding Sensitivity of Differential Attention through the Lens of Adversarial Robustness}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=kwy0cKmTm3},
}

@inproceedings{aaai2026_diversifying_101151,
  title = {{Diversifying Counterattacks: Orthogonal Exploration for Robust CLlP Inference}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37452},
}

@inproceedings{aaai2025_towards_11031,
  title = {{Towards Adversarially Robust Dataset Distillation by Curvature Regularization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32978},
}

@inproceedings{icml2024_benign_43849,
  title = {{Benign Overfitting in Adversarial Training of Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34603},
}