% Key papers for Robustness to poisoning and multiple attacks (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2018_efficient_57081,
  title = {{Efficient Formal Safety Analysis of Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11616},
}

@inproceedings{neurips2017_certified_56190,
  title = {{Certified Defenses for Data Poisoning Attacks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9135},
}

@inproceedings{aaai2020_abstract_102136,
  title = {{Abstract Interpretation of Decision Tree Ensemble Classifiers}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5998},
}

@inproceedings{aaai2023_formalising_3717,
  title = {{Formalising the Robustness of Counterfactual Explanations for Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26740},
}

@inproceedings{iclr2019_combinatorial_29015,
  title = {{Combinatorial Attacks on Binarized Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/860},
}

@inproceedings{aaai2022_robust_2658,
  title = {{Robust Optimal Classification Trees against Adversarial Examples}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08520-robust-optimal-classification-trees-against-adversarial-examples/},
}

@inproceedings{aaai2020_finding_100269,
  title = {{Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5442},
}

@inproceedings{icml2019_the_37800,
  title = {{The advantages of multiple classes for reducing overfitting from test set reuse}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4321},
}

@inproceedings{aaai2024_promoting_7007,
  title = {{Promoting Counterfactual Robustness through Diversity}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30127},
}

@inproceedings{aaai2023_tricking_4879,
  title = {{Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25882},
}

@inproceedings{cvpr2019_knowing_14356,
  title = {{Knowing When to Stop: Evaluation and Verification of Conformity to Output-Size Specifications}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{icml2021_expressive_39324,
  title = {{Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9793},
}

@inproceedings{aaai2024_friendly_6054,
  title = {{Friendly Attacks to Improve Channel Coding Reliability}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29230},
}

@inproceedings{icml2022_on_40915,
  title = {{On Collective Robustness of Bagging Against Data Poisoning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17411},
}

@inproceedings{aaai2024_rethinking_7136,
  title = {{Rethinking Robustness of Model Attributions}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28047},
}

@inproceedings{icml2022_an_40242,
  title = {{An Equivalence Between Data Poisoning and Byzantine Gradient Attacks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17453},
}

@inproceedings{aaai2023_enhancing_3579,
  title = {{Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26065},
}

@inproceedings{aaai2024_coevolutionary_5414,
  title = {{Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30188},
}

@inproceedings{aaai2024_robustness_7185,
  title = {{Robustness Verification of Multi-Class Tree Ensembles}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30093},
}

@inproceedings{aaai2025_prosac_10252,
  title = {{PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32300},
}

@inproceedings{aaai2025_cultivating_8575,
  title = {{Cultivating Archipelago of Forests: Evolving Robust Decision Trees Through Island Coevolution}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34476},
}

@inproceedings{icml2024_trustworthy_45835,
  title = {{Trustworthy Actionable Perturbations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32617},
}

@inproceedings{icml2026_safety_54811,
  title = {{Safety-Efficacy Trade Off: Robustness against Data-Poisoning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=suwjabuQDY},
}

@inproceedings{neurips2025_mibp_76687,
  title = {{MIBP-Cert: Certified Training against Data Perturbations with Mixed-Integer Bilinear Programs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=M2HxuxqNrb},
}

@inproceedings{aaai2026_certified_104620,
  title = {{Certified but Fooled! Breaking Certified Defenses with Ghost Certificates}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37924},
}

@inproceedings{neurips2026_certified_92234,
  title = {{Certified Single-Level Reformulation for Tri-Level Cyber-Physical Grid Security}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/152235},
}