% Key papers for Statistical perspectives on generalization (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2019_analogies_37156,
  title = {{Analogies Explained: Towards Understanding Word Embeddings}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4051},
}

@inproceedings{neurips2017_model_56480,
  title = {{Model-Powered Conditional Independence Test}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9080},
}

@inproceedings{neurips2017_consistent_56209,
  title = {{Consistent Multitask Learning with Nonlinear Output Relations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8987},
}

@inproceedings{icml2017_learning_36258,
  title = {{Learning Latent Space Models with Angular Constraints}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/499},
}

@inproceedings{neurips2019_on_58728,
  title = {{On the Downstream Performance of Compressed Word Embeddings}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14602},
}

@inproceedings{neurips2020_a_59234,
  title = {{A Closer Look at the Training Strategy for Modern Meta-Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17236},
}

@inproceedings{aaai2017_ermma_105701,
  title = {{ERMMA: Expected Risk Minimization for Matrix Approximation-based Recommender Systems}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10743},
}

@inproceedings{aaai2023_stable_4726,
  title = {{Stable Learning via Sparse Variable Independence}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26303},
}

@inproceedings{icml2023_transformers_41811,
  title = {{Transformers as Algorithms: Generalization and Stability in In-context Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24149},
}

@inproceedings{neurips2019_margin_58575,
  title = {{Margin-Based Generalization Lower Bounds for Boosted Classifiers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14166},
}

@inproceedings{icml2021_provably_39831,
  title = {{Provably Strict Generalisation Benefit for Equivariant Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10385},
}

@inproceedings{aaai2020_information_101870,
  title = {{Information-Theoretic Understanding of Population Risk Improvement with Model Compression}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5730},
}

@inproceedings{icml2022_model_40836,
  title = {{Model Agnostic Sample Reweighting for Out-of-Distribution Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17867},
}

@inproceedings{neurips2021_algorithmic_62912,
  title = {{Algorithmic stability and generalization of an unsupervised feature selection algorithm}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27975},
}

@inproceedings{neurips2022_deep_65376,
  title = {{Deep Active Learning by Leveraging Training Dynamics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54785},
}

@inproceedings{neurips2019_a_57833,
  title = {{A Refined Margin Distribution Analysis for Forest Representation Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13681},
}

@inproceedings{neurips2020_learning_60221,
  title = {{Learning to Approximate a Bregman Divergence}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18884},
}

@inproceedings{icml2018_nonoverlap_36878,
  title = {{Nonoverlap-Promoting Variable Selection}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1886},
}

@inproceedings{icml2019_learning_37493,
  title = {{Learning and Data Selection in Big Datasets}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3733},
}

@inproceedings{icml2024_restoring_45488,
  title = {{Restoring balance: principled under/oversampling of data for optimal classification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32963},
}

@inproceedings{neurips2024_a_73543,
  title = {{A Canonicalization Perspective on Invariant and Equivariant Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93936},
}

@inproceedings{neurips2024_transformers_73365,
  title = {{Transformers are Minimax Optimal Nonparametric In-Context Learners}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94083},
}

@inproceedings{iclr2024_sample_34526,
  title = {{Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18548},
}

@inproceedings{neurips2024_fine_73293,
  title = {{Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94148},
}

@inproceedings{neurips2025_subsampled_77571,
  title = {{Subsampled Ensemble Can Improve Generalization Tail Exponentially}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=VJn4UKnEYS},
}

@inproceedings{iclr2024_understanding_33841,
  title = {{Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19234},
}

@inproceedings{icml2024_an_44247,
  title = {{An Information-Theoretic Analysis of In-Context Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34205},
}

@inproceedings{icml2024_statistical_45114,
  title = {{Statistical Properties of Robust Satisficing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33338},
}

@inproceedings{icml2024_model_45658,
  title = {{Model Assessment and Selection under Temporal Distribution Shift}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32794},
}

@inproceedings{neurips2024_adjust_70740,
  title = {{Adjust Pearson's \$r\$ to Measure Arbitrary Monotone Dependence}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96384},
}

@inproceedings{neurips2024_bridging_72925,
  title = {{Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94486},
}

@inproceedings{neurips2024_sketchy_74549,
  title = {{Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93035},
}

@inproceedings{aaai2026_generalizing_106667,
  title = {{Generalizing Analogical Inference from Boolean to Continuous Domains}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38974},
}

@inproceedings{aaai2026_towards_107372,
  title = {{Towards Understanding In-Context Learning of Transformers Under Non-I.I.D. Scenarios}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39724},
}

@inproceedings{aaai2026_rademacher_107795,
  title = {{Rademacher Complexity for Distributionally Robust Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40147},
}

@inproceedings{iclr2024_understanding_34697,
  title = {{Understanding prompt engineering may not require rethinking generalization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18377},
}

@inproceedings{icml2024_a_45700,
  title = {{A Unified Framework for Learning with Nonlinear Model Classes from Arbitrary Linear Samples}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32752},
}

@inproceedings{icml2025_generalization_47608,
  title = {{Generalization and Robustness of the Tilted Empirical Risk}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44989},
}

@inproceedings{icml2025_fairness_48792,
  title = {{Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43830},
}