% Key papers for Neural network robustness verification (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2019_evaluating_28959,
  title = {{Evaluating Robustness of Neural Networks with Mixed Integer Programming}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/817},
}

@inproceedings{iclr2019_benchmarking_28923,
  title = {{Benchmarking Neural Network Robustness to Common Corruptions and Perturbations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/731},
}

@inproceedings{icml2018_differentiable_36634,
  title = {{Differentiable Abstract Interpretation for Provably Robust Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2477},
}

@inproceedings{neurips2018_fast_57130,
  title = {{Fast and Effective Robustness Certification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/12021},
}

@inproceedings{aaai2018_verifying_106131,
  title = {{Verifying Properties of Binarized Deep Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/12206},
}

@inproceedings{iclr2018_evaluating_28448,
  title = {{Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/97},
}

@inproceedings{neurips2018_lipschitz_57343,
  title = {{Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11632},
}

@inproceedings{neurips2019_a_57797,
  title = {{A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14011},
}

@inproceedings{iclr2019_training_28765,
  title = {{Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1028},
}

@inproceedings{iclr2019_boosting_28910,
  title = {{Boosting Robustness Certification of Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/818},
}

@inproceedings{cvpr2020_towards_16370,
  title = {{Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{neurips2019_certifying_57988,
  title = {{Certifying Geometric Robustness of Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14429},
}

@inproceedings{cvpr2020_how_15657,
  title = {{How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{aaai2019_recurjac_101807,
  title = {{RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4522},
}

@inproceedings{icml2019_popqorn_37642,
  title = {{POPQORN: Quantifying Robustness of Recurrent Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3701},
}

@inproceedings{cvpr2021_achieving_16560,
  title = {{Achieving Robustness in Classification Using Optimal Transport With Hinge Regularization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{iclr2020_towards_29705,
  title = {{Towards Stable and Efficient Training of Verifiably Robust Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=Skxuk1rFwB},
}

@inproceedings{iclr2020_on_29235,
  title = {{On Robustness of Neural Ordinary Differential Equations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=B1e9Y2NYvS},
}

@inproceedings{aaai2020_fastened_102082,
  title = {{Fastened CROWN: Tightened Neural Network Robustness Certificates}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5944},
}

@inproceedings{icml2019_proven_37643,
  title = {{PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4021},
}

@inproceedings{aaai2024_galileo_6100,
  title = {{GaLileo: General Linear Relaxation Framework for Tightening Robustness Certification of Transformers}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30180},
}

@inproceedings{aaai2024_lyapunov_6555,
  title = {{Lyapunov-Stable Deep Equilibrium Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29044},
}

@inproceedings{neurips2024_verifiably_70546,
  title = {{Verifiably Robust Conformal Prediction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96567},
}

@inproceedings{aaai2024_accelerating_5111,
  title = {{Accelerating Adversarially Robust Model Selection for Deep Neural Networks via Racing}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30121},
}

@inproceedings{iclr2024_provably_33862,
  title = {{Provably Robust Conformal Prediction with Improved Efficiency}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19213},
}

@inproceedings{iclr2024_colep_34622,
  title = {{COLEP: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic Circuits}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18452},
}

@inproceedings{iclr2024_expressive_35200,
  title = {{Expressive Losses for Verified Robustness via Convex Combinations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17873},
}

@inproceedings{iclr2024_a_35366,
  title = {{A Recipe for Improved Certifiable Robustness}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17707},
}

@inproceedings{icml2024_et_44398,
  title = {{Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34054},
}

@inproceedings{iclr2025_towards_83871,
  title = {{Towards Certification of Uncertainty Calibration under Adversarial Attacks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27949},
}

@inproceedings{icml2024_relational_43982,
  title = {{Relational DNN Verification With Cross Executional Bound Refinement}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34470},
}

@inproceedings{icml2024_robust_44228,
  title = {{Robust Yet Efficient Conformal Prediction Sets}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34224},
}

@inproceedings{icml2024_compositional_44416,
  title = {{Compositional Curvature Bounds for Deep Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34036},
}

@inproceedings{icml2025_probably_47465,
  title = {{Probably Approximately Global Robustness Certification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45128},
}

@inproceedings{icml2025_levis_47566,
  title = {{LEVIS: Large Exact Verifiable Input Spaces for Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45030},
}

@inproceedings{icml2025_no_47899,
  title = {{No Soundness in the Real World: On the Challenges of the Verification of Deployed Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44704},
}

@inproceedings{icml2025_efficient_48985,
  title = {{Efficient Robust Conformal Prediction via Lipschitz-Bounded Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43638},
}

@inproceedings{neurips2024_relational_72509,
  title = {{Relational Verification Leaps Forward with RABBit}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94855},
}

@inproceedings{iclr2025_verifying_81090,
  title = {{Verifying Properties of Binary Neural Networks Using Sparse Polynomial Optimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30678},
}

@inproceedings{iclr2025_support_83467,
  title = {{Support is All You Need for Certified VAE Training}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28349},
}