% Key papers for Sample complexity of learning distributions (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2017_being_36083,
  title = {{Being Robust (in High Dimensions) Can Be Practical}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/460},
}

@inproceedings{neurips2019_data_58088,
  title = {{Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14580},
}

@inproceedings{icml2017_algorithmic_36058,
  title = {{Algorithmic Stability and Hypothesis Complexity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/650},
}

@inproceedings{neurips2018_nearly_57420,
  title = {{Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11343},
}

@inproceedings{neurips2019_sparse_59000,
  title = {{Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14559},
}

@inproceedings{neurips2018_robust_57587,
  title = {{Robust Learning of Fixed-Structure Bayesian Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11973},
}

@inproceedings{neurips2017_learning_56422,
  title = {{Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9415},
}

@inproceedings{iclr2021_why_30706,
  title = {{Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets?}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3299},
}

@inproceedings{neurips2016_supervised_56028,
  title = {{Supervised learning through the lens of compression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7217},
}

@inproceedings{icml2018_learning_36784,
  title = {{Learning Diffusion using Hyperparameters}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1880},
}

@inproceedings{neurips2019_on_58732,
  title = {{On the Hardness of Robust Classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14555},
}

@inproceedings{neurips2020_efficient_59765,
  title = {{Efficient Learning of Discrete Graphical Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18193},
}

@inproceedings{neurips2017_learning_56425,
  title = {{Learning Low-Dimensional Metrics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9194},
}

@inproceedings{neurips2019_sample_58940,
  title = {{Sample Complexity of Learning Mixture of Sparse Linear Regressions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14062},
}

@inproceedings{neurips2020_on_60465,
  title = {{On Learning Ising Models under Huber's Contamination Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17436},
}

@inproceedings{neurips2021_towards_63009,
  title = {{Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28640},
}

@inproceedings{aaai2022_towards_2852,
  title = {{Towards Bridging Sample Complexity and Model Capacity}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/01972-towards-bridging-sample-complexity-and-model-capacity/},
}

@inproceedings{neurips2020_private_60614,
  title = {{Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17647},
}

@inproceedings{aaai2018_approximate_103380,
  title = {{Approximate Inference via Weighted Rademacher Complexity}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/12127},
}

@inproceedings{iclr2020_learning_29247,
  title = {{Learning to Guide Random Search}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=B1gHokBKwS},
}

@inproceedings{neurips2024_on_73010,
  title = {{On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94411},
}

@inproceedings{neurips2024_learning_74277,
  title = {{Learning from higher-order correlations, efficiently: hypothesis tests, random features, and neural networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93280},
}

@inproceedings{neurips2024_predicting_74581,
  title = {{Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93006},
}

@inproceedings{iclr2024_learning_34402,
  title = {{Learning Hierarchical Polynomials with Three-Layer Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18673},
}

@inproceedings{icml2024_sample_45537,
  title = {{Sample Complexity Bounds for Estimating Probability Divergences under Invariances}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32915},
}

@inproceedings{iclr2024_optimal_34289,
  title = {{Optimal Sample Complexity of Contrastive Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18786},
}

@inproceedings{icml2024_hierarchical_44874,
  title = {{Hierarchical Integral Probability Metrics: A distance on random probability measures with low sample complexity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33578},
}

@inproceedings{icml2024_learning_44986,
  title = {{Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33466},
}

@inproceedings{icml2024_no_45148,
  title = {{No Dimensional Sampling Coresets for Classification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33304},
}

@inproceedings{icml2025_exact_48063,
  title = {{Exact Recovery of Sparse Binary Vectors from Generalized Linear Measurements}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44544},
}

@inproceedings{icml2025_learning_48113,
  title = {{Learning Gaussian DAG Models without Condition Number Bounds}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44495},
}

@inproceedings{icml2025_sample_48532,
  title = {{Sample Complexity of Correlation Detection in the Gaussian Wigner Model}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44089},
}

@inproceedings{icml2026_token_51234,
  title = {{Token Sample Complexity of Attention}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=K5blb595KX},
}

@inproceedings{icml2026_sample_54316,
  title = {{Sample Complexity Bounds for Robust Mean Estimation with Mean-Shift Contamination}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=no9dQDBxsu},
}

@inproceedings{icml2026_deep_54347,
  title = {{Deep Learning of Compositional Targets with Hierarchical Spectral Methods}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=o4w4LRrIMT},
}

@inproceedings{neurips2024_a_70454,
  title = {{A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96653},
}

@inproceedings{neurips2024_learning_71196,
  title = {{Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96035},
}

@inproceedings{neurips2024_improved_71974,
  title = {{Improved Sample Complexity Bounds for Diffusion Model Training}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95337},
}

@inproceedings{neurips2024_transductive_72691,
  title = {{Transductive Learning is Compact}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94694},
}

@inproceedings{neurips2025_dimension_75059,
  title = {{Dimension-free Score Matching and Time Bootstrapping for Diffusion Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=3p06mDUV5O},
}