% Key papers for PAC-Bayes bounds (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2018_a_28627,
  title = {{A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/93},
}

@inproceedings{icml2018_meta_36848,
  title = {{Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1976},
}

@inproceedings{neurips2018_pac_57495,
  title = {{PAC-Bayes bounds for stable algorithms with instance-dependent priors}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11878},
}

@inproceedings{neurips2019_fast_58271,
  title = {{Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14083},
}

@inproceedings{iclr2022_pac_31582,
  title = {{PAC-Bayes Information Bottleneck}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6238},
}

@inproceedings{neurips2020_pac_60556,
  title = {{PAC-Bayesian Bound for the Conditional Value at Risk}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17782},
}

@inproceedings{neurips2021_bridging_61313,
  title = {{Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28476},
}

@inproceedings{neurips2019_pac_58780,
  title = {{PAC-Bayes Un-Expected Bernstein Inequality}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14184},
}

@inproceedings{aaai2020_meta_101979,
  title = {{Meta-Learning PAC-Bayes Priors in Model Averaging}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5841},
}

@inproceedings{neurips2020_a_59254,
  title = {{A Limitation of the PAC-Bayes Framework}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18438},
}

@inproceedings{neurips2022_pac_65974,
  title = {{PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53687},
}

@inproceedings{neurips2024_a_70835,
  title = {{A Unified Recipe for Deriving (Time-Uniform) PAC-Bayes Bounds}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/98308},
}

@inproceedings{aaai2020_improved_102158,
  title = {{Improved PAC-Bayesian Bounds for Linear Regression}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6020},
}

@inproceedings{neurips2020_pac_60554,
  title = {{PAC-Bayes Analysis Beyond the Usual Bounds}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16891},
}

@inproceedings{aaai2019_dimension_101602,
  title = {{Dimension-Free Error Bounds from Random Projections}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4300},
}

@inproceedings{icml2023_pac_42036,
  title = {{PAC-Bayesian Generalization Bounds for Adversarial Generative Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24598},
}

@inproceedings{neurips2022_integral_64479,
  title = {{Integral Probability Metrics PAC-Bayes Bounds}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53658},
}

@inproceedings{neurips2020_pac_60555,
  title = {{PAC-Bayes Learning Bounds for Sample-Dependent Priors}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18693},
}

@inproceedings{neurips2021_learning_61251,
  title = {{Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28787},
}

@inproceedings{neurips2022_a_64033,
  title = {{A PAC-Bayesian Generalization Bound for Equivariant Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52800},
}

@inproceedings{neurips2024_pac_71105,
  title = {{PAC-Bayes-Chernoff bounds for unbounded losses}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96111},
}

@inproceedings{icml2024_more_45427,
  title = {{More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33024},
}

@inproceedings{icml2025_pac_48016,
  title = {{PAC-Bayes Analysis for Recalibration in Classification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44590},
}

@inproceedings{icml2026_symmetries_51781,
  title = {{Symmetries in PAC-Bayesian Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Ps37H7zPDj},
}

@inproceedings{neurips2024_learning_71479,
  title = {{Learning via Surrogate PAC-Bayes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95781},
}

@inproceedings{neurips2024_controlling_73707,
  title = {{Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93790},
}

@inproceedings{neurips2025_fast_76852,
  title = {{Fast Rate Bounds for Multi-Task and Meta-Learning with Different Sample Sizes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=NYcnZaA4vO},
}

@inproceedings{neurips2025_pac_77262,
  title = {{PAC-Bayes Bounds for Multivariate Linear Regression and Linear Autoencoders}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=S1zkFSby8G},
}

@inproceedings{neurips2025_certifying_77375,
  title = {{Certifying Deep Network Risks and Individual Predictions with PAC-Bayes Loss via Localized Priors}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=T6d5IYr8PJ},
}

@inproceedings{iclr2026_block_88667,
  title = {{Block-Sample MAC-Bayes Generalization Bounds}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=pelxC06W8U},
}

@inproceedings{neurips2026_tight_93646,
  title = {{Tight PAC-Bayes Generalisation Guarantees for Large Language Model Safety Monitoring}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/150040},
}