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

@inproceedings{neurips2020_automatic_59411,
  title = {{Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18867},
}

@inproceedings{iclr2020_certified_29332,
  title = {{Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=BkeWw6VFwr},
}

@inproceedings{icml2020_scalable_38731,
  title = {{Scalable Differential Privacy with Certified Robustness in Adversarial Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6401},
}

@inproceedings{iclr2023_certified_32381,
  title = {{(Certified!!) Adversarial Robustness for Free!}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11081},
}

@inproceedings{iclr2023_densepure_33191,
  title = {{DensePure: Understanding Diffusion Models for Adversarial Robustness}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11007},
}

@inproceedings{neurips2024_diffusion_74404,
  title = {{Diffusion Models are Certifiably Robust Classifiers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93165},
}

@inproceedings{neurips2022_rethinking_66418,
  title = {{Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54910},
}

@inproceedings{iclr2023_certified_32116,
  title = {{Certified Training: Small Boxes are All You Need}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/10739},
}

@inproceedings{aaai2022_towards_2851,
  title = {{Towards Automating Model Explanations with Certified Robustness Guarantees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/06935-towards-automating-model-explanations-with-certified-robustness-guarantees/},
}

@inproceedings{iclr2022_on_31768,
  title = {{On the Certified Robustness for Ensemble Models and Beyond}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6874},
}

@inproceedings{aaai2024_provable_7021,
  title = {{Provable Robustness against a Union of L\_0 Adversarial Attacks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30106},
}

@inproceedings{iclr2022_almost_31547,
  title = {{Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6398},
}

@inproceedings{aaai2021_fast_603,
  title = {{Fast Training of Provably Robust Neural Networks by SingleProp}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/06803-fast-training-of-provably-robust-neural-networks-by-singleprop/},
}

@inproceedings{cvpr2025_certified_25687,
  title = {{Certified Human Trajectory Prediction}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34812},
}

@inproceedings{neurips2023_connecting_68220,
  title = {{Connecting Certified and Adversarial Training}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71575},
}

@inproceedings{iclr2020_universal_29248,
  title = {{Universal Approximation with Certified Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=B1gX8kBtPr},
}

@inproceedings{iclr2022_policy_31652,
  title = {{Policy Smoothing for Provably Robust Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6719},
}

@inproceedings{neurips2021_center_63336,
  title = {{Center Smoothing: Certified Robustness for Networks with Structured Outputs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26634},
}

@inproceedings{iclr2021_certify_30353,
  title = {{Certify or Predict: Boosting Certified Robustness with Compositional Architectures}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3359},
}

@inproceedings{iclr2023_on_33207,
  title = {{On Explaining Neural Network Robustness with Activation Path}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11195},
}

@inproceedings{neurips2024_certified_72583,
  title = {{Certified Robustness for Deep Equilibrium Models via Serialized Random Smoothing}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94791},
}

@inproceedings{icml2024_breaking_44574,
  title = {{Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33878},
}

@inproceedings{icml2025_position_46766,
  title = {{Position: Certified Robustness Does Not (Yet) Imply Model Security}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/40159},
}

@inproceedings{icml2026_rethinking_52647,
  title = {{Rethinking Evaluation Paradigms in IBP-based Certified Training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=XoaS2r1ezP},
}

@inproceedings{iclr2025_certified_82761,
  title = {{Certified Robustness Under Bounded Levenshtein Distance}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29038},
}

@inproceedings{iclr2024_certified_33652,
  title = {{Certified Adversarial Robustness for Rate Encoded Spiking Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19423},
}