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

@inproceedings{neurips2017_information_56387,
  title = {{Information-theoretic analysis of generalization capability of learning algorithms}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9039},
}

@inproceedings{neurips2019_uniform_59142,
  title = {{Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13299},
}

@inproceedings{neurips2020_sharpened_60825,
  title = {{Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17056},
}

@inproceedings{neurips2018_multi_57396,
  title = {{Multi-Class Learning: From Theory to Algorithm}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11173},
}

@inproceedings{neurips2018_generalization_57160,
  title = {{Generalization Bounds for Uniformly Stable Algorithms}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11926},
}

@inproceedings{neurips2021_tighter_63517,
  title = {{Tighter Expected Generalization Error Bounds via Wasserstein Distance}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28381},
}

@inproceedings{neurips2020_conditioning_59566,
  title = {{Conditioning and Processing: Techniques to Improve Information-Theoretic Generalization Bounds}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/19051},
}

@inproceedings{aaai2019_certifying_101315,
  title = {{Certifying the True Error: Machine Learning in Coq with Verified Generalization Guarantees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4115},
}

@inproceedings{iclr2020_understanding_29842,
  title = {{Understanding Generalization in Recurrent Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=rkgg6xBYDH},
}

@inproceedings{icml2018_dropout_36655,
  title = {{Dropout Training, Data-dependent Regularization, and Generalization Bounds}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2000},
}

@inproceedings{neurips2020_learning_60153,
  title = {{Learning Bounds for Risk-sensitive Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17509},
}

@inproceedings{icml2021_risk_39886,
  title = {{Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10623},
}

@inproceedings{neurips2021_generalization_61512,
  title = {{Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27288},
}

@inproceedings{neurips2021_information_61975,
  title = {{Information-theoretic generalization bounds for black-box learning algorithms}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28329},
}

@inproceedings{neurips2021_an_62460,
  title = {{An Exact Characterization of the Generalization Error for the Gibbs Algorithm}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26868},
}

@inproceedings{iclr2021_generalization_30122,
  title = {{Generalization bounds via distillation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3261},
}

@inproceedings{neurips2018_chaining_56939,
  title = {{Chaining Mutual Information and Tightening Generalization Bounds}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11697},
}

@inproceedings{aaai2024_improving_6300,
  title = {{Improving Neural Network Generalization on Data-Limited Regression with Doubly-Robust Boosting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30071},
}

@inproceedings{iclr2023_stabledr_31988,
  title = {{StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11206},
}

@inproceedings{neurips2019_hypothesis_58384,
  title = {{Hypothesis Set Stability and Generalization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13779},
}

@inproceedings{neurips2024_information_74595,
  title = {{Information-theoretic Generalization Analysis for Expected Calibration Error}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92994},
}

@inproceedings{iclr2024_fantastic_34302,
  title = {{Fantastic Generalization Measures are Nowhere to be Found}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18773},
}

@inproceedings{neurips2024_generalization_73717,
  title = {{Generalization Error Bounds for Two-stage Recommender Systems with Tree Structure}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93782},
}

@inproceedings{neurips2024_generalization_73898,
  title = {{Generalization Bounds via Conditional \$f\$-Information}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93613},
}

@inproceedings{iclr2024_koopman_34131,
  title = {{Koopman-based generalization bound: New aspect for full-rank weights}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18944},
}

@inproceedings{icml2024_generalization_44484,
  title = {{Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33968},
}

@inproceedings{icml2024_h_45348,
  title = {{\$H\$-Consistency Guarantees for Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33103},
}

@inproceedings{icml2024_slicing_45628,
  title = {{Slicing Mutual Information Generalization Bounds for Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32824},
}

@inproceedings{icml2025_provable_46395,
  title = {{Provable Length Generalization in Sequence Prediction via Spectral Filtering}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46169},
}

@inproceedings{icml2025_non_47586,
  title = {{Non-Asymptotic Length Generalization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45010},
}

@inproceedings{icml2025_understanding_48253,
  title = {{Understanding Generalization in Quantum Machine Learning with Margins}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44359},
}

@inproceedings{icml2026_harmful_51680,
  title = {{Harmful Overfitting in Sobolev Spaces}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=OmmKEy9Ixf},
}

@inproceedings{icml2026_stability_52048,
  title = {{Stability beyond Bounded Differences: Sharp Generalization Bounds under Finite \$L\_p\$ Moments}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=SGTLVjx3MN},
}

@inproceedings{icml2026_length_52589,
  title = {{Length Generalization Bounds for Transformers}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=XNXU8BJRT7},
}

@inproceedings{icml2026_generalization_53497,
  title = {{Generalization of Gibbs and Langevin Monte Carlo Algorithms in the Interpolation Regime}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=g2emeL0i3t},
}

@inproceedings{icml2026_tight_53770,
  title = {{Tight Margin-Based Generalization Bounds for Voting Classifiers over Finite Hypothesis Sets}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=iVj5Rdwb6I},
}

@inproceedings{neurips2024_generalization_70164,
  title = {{Generalization of Hamiltonian algorithms}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96919},
}

@inproceedings{neurips2025_algorithm_76793,
  title = {{Algorithm- and Data-Dependent Generalization Bounds for Diffusion Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=N4OFsehhi7},
}

@inproceedings{neurips2025_learning_77054,
  title = {{Learning from Interval Targets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Q5bJZArYUZ},
}