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

@inproceedings{neurips2020_on_60488,
  title = {{On the Theory of Transfer Learning: The Importance of Task Diversity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18053},
}

@inproceedings{iclr2021_few_30640,
  title = {{Few-Shot Learning via Learning the Representation, Provably}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2535},
}

@inproceedings{aaai2017_scanpath_104581,
  title = {{Scanpath Complexity: Modeling Reading Effort Using Gaze Information}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11159},
}

@inproceedings{neurips2017_collaborative_56198,
  title = {{Collaborative PAC Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9026},
}

@inproceedings{aaai2021_incentive_774,
  title = {{Incentive-Aware PAC Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/05797-incentive-aware-pac-learning/},
}

@inproceedings{neurips2019_preference_58827,
  title = {{Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13960},
}

@inproceedings{neurips2020_towards_60999,
  title = {{Towards Understanding Hierarchical Learning: Benefits of Neural Representations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18336},
}

@inproceedings{neurips2016_lifelong_55848,
  title = {{Lifelong Learning with Weighted Majority Votes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7092},
}

@inproceedings{neurips2022_learning_66386,
  title = {{Learning single-index models with shallow neural networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53940},
}

@inproceedings{neurips2019_multiclass_58640,
  title = {{Multiclass Learning from Contradictions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13903},
}

@inproceedings{icml2021_learning_39548,
  title = {{Learning from Noisy Labels with No Change to the Training Process}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9933},
}

@inproceedings{neurips2018_tight_57710,
  title = {{Tight Bounds for Collaborative PAC Learning via Multiplicative Weights}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11360},
}

@inproceedings{aaai2018_sample_102360,
  title = {{Sample-Efficient Learning of Mixtures}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11627},
}

@inproceedings{neurips2022_statistically_65103,
  title = {{Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53296},
}

@inproceedings{icml2020_on_38559,
  title = {{On the Sample Complexity of Adversarial Multi-Source PAC Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6839},
}

@inproceedings{neurips2022_an_66038,
  title = {{An Information-Theoretic Framework for Deep Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53998},
}

@inproceedings{aaai2024_learning_6438,
  title = {{Learning Broadcast Protocols}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29089},
}

@inproceedings{icml2019_pac_37640,
  title = {{PAC Learnability of Node Functions in Networked Dynamical Systems}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4122},
}

@inproceedings{icml2020_on_38535,
  title = {{On Learning Language-Invariant Representations for Universal Machine Translation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6658},
}

@inproceedings{icml2020_refined_38684,
  title = {{Refined bounds for algorithm configuration: The knife-edge of dual class approximability}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5912},
}

@inproceedings{neurips2024_provably_70684,
  title = {{Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96439},
}

@inproceedings{iclr2024_role_33831,
  title = {{Role of Locality and Weight Sharing in Image-Based Tasks: A Sample Complexity Separation between CNNs, LCNs, and FCNs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19244},
}

@inproceedings{neurips2024_improved_73636,
  title = {{Improved Sample Complexity for Multiclass PAC Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93856},
}

@inproceedings{neurips2024_generalizablity_74139,
  title = {{Generalizablity of Memorization Neural Network}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93400},
}

@inproceedings{neurips2024_pretrained_74276,
  title = {{Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93281},
}

@inproceedings{icml2024_efficient_43345,
  title = {{Efficient PAC Learnability of Dynamical Systems Over Multilayer Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35107},
}

@inproceedings{icml2024_collaborative_44504,
  title = {{Collaborative Learning with Different Labeling Functions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33948},
}

@inproceedings{icml2024_guarantees_45815,
  title = {{Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32637},
}

@inproceedings{icml2025_fundamental_47134,
  title = {{Fundamental limits of learning in sequence multi-index models and deep attention networks: high-dimensional asymptotics and sharp thresholds}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45453},
}

@inproceedings{icml2026_universal_50384,
  title = {{Universal Multiclass Transductive Online Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Bnr4cjW6XG},
}

@inproceedings{icml2026_when_51252,
  title = {{When More Data Doesn't Help: Limits of Adaptation in Multitask Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=KFIW6LOeT1},
}

@inproceedings{neurips2024_on_70219,
  title = {{On the Computational Landscape of Replicable Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96867},
}

@inproceedings{neurips2024_on_70437,
  title = {{On the Complexity of Teaching a Family of Linear Behavior Cloning Learners}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96669},
}

@inproceedings{neurips2024_a_71179,
  title = {{A Theory of Optimistically Universal Online Learnability for General Concept Classes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96050},
}

@inproceedings{neurips2024_learning_73323,
  title = {{Learning to Understand: Identifying Interactions via the Möbius Transform}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94122},
}

@inproceedings{neurips2024_learning_73961,
  title = {{Learning Partitions from Context}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93551},
}

@inproceedings{neurips2024_derandomizing_74252,
  title = {{Derandomizing Multi-Distribution Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93304},
}

@inproceedings{neurips2024_transformation_74258,
  title = {{Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93298},
}

@inproceedings{neurips2025_efficient_75249,
  title = {{Efficient PAC Learning for Realizable-Statistic Models via Convex Surrogates}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=5qdIdWflzg},
}