% Key papers for Statistical analysis of GAN estimators (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2020_what_61097,
  title = {{What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18521},
}

@inproceedings{neurips2018_a_56793,
  title = {{A Convex Duality Framework for GANs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11513},
}

@inproceedings{iclr2019_approximability_29151,
  title = {{Approximability of Discriminators Implies Diversity in GANs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/936},
}

@inproceedings{neurips2018_nonparametric_57446,
  title = {{Nonparametric Density Estimation under Adversarial Losses}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/12560},
}

@inproceedings{icml2019_approximation_37163,
  title = {{Approximation and non-parametric estimation of ResNet-type convolutional neural networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3724},
}

@inproceedings{neurips2021_deep_63392,
  title = {{Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26943},
}

@inproceedings{neurips2017_dualing_56270,
  title = {{Dualing GANs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9335},
}

@inproceedings{neurips2019_learning_58478,
  title = {{Learning Distributions Generated by One-Layer ReLU Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13883},
}

@inproceedings{neurips2019_nonparametric_58690,
  title = {{Nonparametric Density Estimation \& Convergence Rates for GANs under Besov IPM Losses}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13231},
}

@inproceedings{icml2020_on_38539,
  title = {{On Relativistic f-Divergences}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6142},
}

@inproceedings{neurips2021_bounds_61820,
  title = {{Bounds all around: training energy-based models with bidirectional bounds}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27738},
}

@inproceedings{iclr2019_robust_28772,
  title = {{ROBUST ESTIMATION VIA GENERATIVE ADVERSARIAL NETWORKS}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1133},
}

@inproceedings{iclr2022_minimax_31662,
  title = {{Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/7087},
}

@inproceedings{icml2023_stabilizing_41862,
  title = {{Stabilizing GANs' Training with Brownian Motion Controller}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24275},
}

@inproceedings{neurips2021_non_61868,
  title = {{Non-asymptotic Error Bounds for Bidirectional GANs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28862},
}

@inproceedings{neurips2022_a_65291,
  title = {{A Quantitative Geometric Approach to Neural-Network Smoothness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54974},
}

@inproceedings{icml2024_universal_44293,
  title = {{Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34159},
}

@inproceedings{icml2026_transfer_51961,
  title = {{Transfer Learning in Nonparametric Regression with Deep ReLU Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=RVEhUx4fOs},
}

@inproceedings{neurips2018_minimax_57379,
  title = {{Minimax Estimation of Neural Net Distance}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11383},
}

@inproceedings{neurips2024_robust_72335,
  title = {{Robust Mixture Learning when Outliers Overwhelm Small Groups}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95012},
}

@inproceedings{neurips2025_approximation_79521,
  title = {{Approximation and Generalization Abilities of Score-based Neural Network Generative Models for Sub-Gaussian Distributions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=qGYim5iYYG},
}