% Key papers for Provable robust learning guarantees (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2018_provable_36939,
  title = {{Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2136},
}

@inproceedings{iclr2018_certified_28475,
  title = {{Certified Defenses against Adversarial Examples}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/116},
}

@inproceedings{iclr2018_certifying_28527,
  title = {{Certifying Some Distributional Robustness with Principled Adversarial Training}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/147},
}

@inproceedings{neurips2017_formal_56327,
  title = {{Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9014},
}

@inproceedings{icml2019_exploring_37348,
  title = {{Exploring the Landscape of Spatial Robustness}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4080},
}

@inproceedings{cvpr2019_robustness_14751,
  title = {{Robustness via Curvature Regularization, and Vice Versa}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2018_scaling_57608,
  title = {{Scaling provable adversarial defenses}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11803},
}

@inproceedings{neurips2016_robustness_55969,
  title = {{Robustness of classifiers: from adversarial to random noise}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6980},
}

@inproceedings{neurips2018_semidefinite_57617,
  title = {{Semidefinite relaxations for certifying robustness to adversarial examples}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/12028},
}

@inproceedings{neurips2018_adversarial_56851,
  title = {{Adversarial vulnerability for any classifier}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11136},
}

@inproceedings{cvpr2022_on_19499,
  title = {{On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{icml2020_minimally_38453,
  title = {{Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6837},
}

@inproceedings{icml2020_adversarial_37922,
  title = {{Adversarial Robustness Against the Union of Multiple Perturbation Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6411},
}

@inproceedings{iclr2019_generalizable_28985,
  title = {{Generalizable Adversarial Training via Spectral Normalization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/958},
}

@inproceedings{icml2019_first_37375,
  title = {{First-Order Adversarial Vulnerability of Neural Networks and Input Dimension}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4017},
}

@inproceedings{neurips2019_convergence_58046,
  title = {{Convergence of Adversarial Training in Overparametrized Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14616},
}

@inproceedings{cvpr2020_achieving_15104,
  title = {{Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2022_enhancing_18764,
  title = {{Enhancing Adversarial Training With Second-Order Statistics of Weights}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{neurips2017_lower_56458,
  title = {{Lower bounds on the robustness to adversarial perturbations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8875},
}

@inproceedings{cvpr2021_dsrna_16797,
  title = {{DSRNA: Differentiable Search of Robust Neural Architectures}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{aaai2024_clarifying_5403,
  title = {{Clarifying the Behavior and the Difficulty of Adversarial Training}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29032},
}

@inproceedings{cvpr2025_mos_26878,
  title = {{MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33923},
}

@inproceedings{iclr2024_a_34277,
  title = {{A Sublinear Adversarial Training Algorithm}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18798},
}

@inproceedings{icml2024_robust_44337,
  title = {{Robust Universal Adversarial Perturbations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34115},
}

@inproceedings{aaai2025_differentiable_8732,
  title = {{Differentiable Adversarial Attacks for Marked Temporal Point Processes}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33724},
}

@inproceedings{iclr2024_robust_35293,
  title = {{Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17780},
}

@inproceedings{icml2024_towards_45418,
  title = {{Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33033},
}

@inproceedings{neurips2024_high_72336,
  title = {{High-dimensional (Group) Adversarial Training in Linear Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95011},
}

@inproceedings{iclr2024_beyond_33929,
  title = {{Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19146},
}

@inproceedings{icml2025_quadratic_48103,
  title = {{Quadratic Upper Bound for Boosting Robustness}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44505},
}

@inproceedings{icml2025_adversarial_48175,
  title = {{Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44436},
}

@inproceedings{icml2026_moco_51929,
  title = {{MoCo-EA: Exploiting Adversarial Mode Connectivity for Efficient Evolutionary Attacks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=RB4V5TkIOz},
}

@inproceedings{neurips2025_adversarial_74949,
  title = {{Adversarial Robustness of Nonparametric Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=2neULQUK3n},
}

@inproceedings{neurips2025_robust_76022,
  title = {{Robust Satisficing Gaussian Process Bandits Under Adversarial Attacks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Ejcn7IDkzT},
}

@inproceedings{neurips2025_understanding_77868,
  title = {{Understanding and Improving Fast Adversarial Training against \$l\_0\$ Bounded Perturbations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=YOv9CANvDv},
}

@inproceedings{neurips2025_kernel_79286,
  title = {{Kernel Learning with Adversarial Features: Numerical Efficiency and Adaptive Regularization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=nsE0QN904q},
}

@inproceedings{iclr2026_on_88078,
  title = {{On the Tension Between Optimality and Adversarial Robustness in Policy Optimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=ion4VYJWvo},
}

@inproceedings{cvpr2026_the_99616,
  title = {{The Power of Decaying Steps: Enhancing Attack Stability and Transferability for Sign-based Optimizers}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Tao_The_Power_of_Decaying_Steps_Enhancing_Attack_Stability_and_Transferability_CVPR_2026_paper.html},
}

@inproceedings{aaai2026_stochastic_99894,
  title = {{Stochastic Universal Adversarial Perturbations with Fixed Optimization Constraint and Ensured High-probability Transferability}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37015},
}

@inproceedings{iclr2024_a_35697,
  title = {{A ROBUST DIFFERENTIAL NEURAL ODE OPTIMIZER}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17376},
}