% Key papers for Wasserstein losses for generative models (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2019_max_14505,
  title = {{Max-Sliced Wasserstein Distance and Its Use for GANs}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2018_sliced_13596,
  title = {{Sliced Wasserstein Distance for Learning Gaussian Mixture Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Kolouri_Sliced_Wasserstein_Distance_CVPR_2018_paper.html},
}

@inproceedings{neurips2018_minimax_57380,
  title = {{Minimax Statistical Learning with Wasserstein distances}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11276},
}

@inproceedings{cvpr2021_a_16546,
  title = {{A Sliced Wasserstein Loss for Neural Texture Synthesis}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{neurips2018_on_57466,
  title = {{On the Convergence and Robustness of Training GANs with Regularized Optimal Transport}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11683},
}

@inproceedings{neurips2020_a_59284,
  title = {{A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17543},
}

@inproceedings{neurips2018_distilled_57055,
  title = {{Distilled Wasserstein Learning for Word Embedding and Topic Modeling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11185},
}

@inproceedings{icml2018_a_36484,
  title = {{A Two-Step Computation of the Exact GAN Wasserstein Distance}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2363},
}

@inproceedings{icml2019_wasserstein_37852,
  title = {{Wasserstein of Wasserstein Loss for Learning Generative Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4124},
}

@inproceedings{neurips2020_asymptotic_59393,
  title = {{Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18872},
}

@inproceedings{aaai2021_towards_1535,
  title = {{Towards Generalized Implementation of Wasserstein Distance in GANs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/10514-towards-generalized-implementation-of-wasserstein-distance-in-gans/},
}

@inproceedings{neurips2019_asymptotic_57918,
  title = {{Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14483},
}

@inproceedings{aaai2021_swift_1321,
  title = {{SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/06548-swift-scalable-wasserstein-factorization-for-sparse-nonnegative-tensors/},
}

@inproceedings{icml2019_a_37099,
  title = {{A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4293},
}

@inproceedings{neurips2022_score_65983,
  title = {{Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53873},
}

@inproceedings{aaai2022_partial_2540,
  title = {{Partial Wasserstein Covering}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/07115-partial-wasserstein-covering/},
}

@inproceedings{neurips2019_learning_58516,
  title = {{Learning from Bad Data via Generation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13723},
}

@inproceedings{cvpr2021_mongenet_17424,
  title = {{MongeNet: Efficient Sampler for Geometric Deep Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{icml2022_distribution_40466,
  title = {{Distribution Regression with Sliced Wasserstein Kernels}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17913},
}

@inproceedings{icml2023_sliced_43044,
  title = {{Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24890},
}

@inproceedings{neurips2026_mind_91323,
  title = {{MIND: Monge Inception Distance for Generative Models Evaluation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/150991},
}