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

@inproceedings{icml2017_wasserstein_36455,
  title = {{Wasserstein Generative Adversarial Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/799},
}

@inproceedings{neurips2017_mmd_56459,
  title = {{MMD GAN: Towards Deeper Understanding of Moment Matching Network}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9008},
}

@inproceedings{iclr2017_mode_28261,
  title = {{Mode Regularized Generative Adversarial Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=HJKkY35le},
}

@inproceedings{cvpr2018_generative_13152,
  title = {{Generative Modeling Using the Sliced Wasserstein Distance}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Deshpande_Generative_Modeling_Using_CVPR_2018_paper.html},
}

@inproceedings{iclr2019_improving_28853,
  title = {{Improving Generalization and Stability of Generative Adversarial Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/896},
}

@inproceedings{iclr2018_many_28467,
  title = {{Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/180},
}

@inproceedings{neurips2019_modeling_58618,
  title = {{Modeling Tabular data using Conditional GAN}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13822},
}

@inproceedings{neurips2018_adversarial_56847,
  title = {{Adversarial Regularizers in Inverse Problems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11813},
}

@inproceedings{iclr2019_misgan_29013,
  title = {{MisGAN: Learning from Incomplete Data with Generative Adversarial Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1096},
}

@inproceedings{icml2018_is_36762,
  title = {{Is Generator Conditioning Causally Related to GAN Performance?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2439},
}

@inproceedings{neurips2018_invertibility_57251,
  title = {{Invertibility of Convolutional Generative Networks from Partial Measurements}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11915},
}

@inproceedings{icml2019_learning_37480,
  title = {{Learning Generative Models across Incomparable Spaces}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3687},
}

@inproceedings{neurips2017_bayesian_56175,
  title = {{Bayesian GAN}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9145},
}

@inproceedings{neurips2018_bourgan_56924,
  title = {{BourGAN: Generative Networks with Metric Embeddings}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11237},
}

@inproceedings{iclr2020_a_29442,
  title = {{A Closer Look at the Optimization Landscapes of Generative Adversarial Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=HJeVnCEKwH},
}

@inproceedings{iclr2017_on_28202,
  title = {{On the Quantitative Analysis of Decoder-Based Generative Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=B1M8JF9xx},
}

@inproceedings{iclr2019_probgan_28880,
  title = {{ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1088},
}

@inproceedings{iclr2019_discriminator_29000,
  title = {{Discriminator Rejection Sampling}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/938},
}

@inproceedings{neurips2019_conditional_58026,
  title = {{Conditional Independence Testing using Generative Adversarial Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14501},
}

@inproceedings{icml2017_mcgan_36282,
  title = {{McGan: Mean and Covariance Feature Matching GAN}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/705},
}

@inproceedings{aaai2024_neuroevolution_6772,
  title = {{Neuroevolution of a Multi-Generator GAN (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30493},
}

@inproceedings{iclr2024_analyzing_35050,
  title = {{Analyzing and Improving Optimal-Transport-based Adversarial Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18024},
}

@inproceedings{icml2026_who_50685,
  title = {{Who Said Neural Networks Aren't Linear?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Eqm5VEaAom},
}

@inproceedings{iclr2025_discrete_84037,
  title = {{Discrete Distribution Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27788},
}