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

@inproceedings{icml2016_gromov_35858,
  title = {{Gromov-Wasserstein Averaging of Kernel and Distance Matrices}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/peyre16.html},
}

@inproceedings{neurips2018_differential_57034,
  title = {{Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11570},
}

@inproceedings{cvpr2017_order_12521,
  title = {{Order-Preserving Wasserstein Distance for Sequence Matching}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{icml2019_subspace_37775,
  title = {{Subspace Robust Wasserstein Distances}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4320},
}

@inproceedings{icml2018_stochastic_37013,
  title = {{Stochastic Wasserstein Barycenters}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2316},
}

@inproceedings{neurips2018_generalizing_57165,
  title = {{Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11969},
}

@inproceedings{neurips2021_the_61271,
  title = {{The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28096},
}

@inproceedings{cvpr2020_synchronizing_16312,
  title = {{Synchronizing Probability Measures on Rotations via Optimal Transport}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{icml2021_scalable_39921,
  title = {{Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8951},
}

@inproceedings{iclr2021_wasserstein_30454,
  title = {{Wasserstein-2 Generative Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2664},
}

@inproceedings{neurips2019_massively_58578,
  title = {{Massively scalable Sinkhorn distances via the Nyström method}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13597},
}

@inproceedings{iclr2019_wasserstein_28870,
  title = {{Wasserstein Barycenter Model Ensembling}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1048},
}

@inproceedings{icml2021_a_38981,
  title = {{A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8477},
}

@inproceedings{neurips2019_subspace_59040,
  title = {{Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13793},
}

@inproceedings{aaai2017_s2jsd_105867,
  title = {{S2JSD-LSH: A Locality-Sensitive Hashing Schema for Probability Distributions}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10989},
}

@inproceedings{iclr2021_continuous_29989,
  title = {{Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2713},
}

@inproceedings{neurips2020_quantifying_60650,
  title = {{Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17618},
}

@inproceedings{cvpr2022_computing_18493,
  title = {{Computing Wasserstein-p Distance Between Images With Linear Cost}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{neurips2018_dimensionality_57044,
  title = {{Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11808},
}

@inproceedings{neurips2019_interior_58425,
  title = {{Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13791},
}

@inproceedings{icml2024_a_45384,
  title = {{A New Robust Partial p-Wasserstein-Based Metric for Comparing Distributions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33067},
}

@inproceedings{neurips2024_semidefinite_74093,
  title = {{Semidefinite Relaxations of the Gromov-Wasserstein Distance}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93440},
}

@inproceedings{iclr2024_generative_34574,
  title = {{Generative Sliced MMD Flows with Riesz Kernels}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18500},
}

@inproceedings{neurips2024_wasserstein_72771,
  title = {{Wasserstein convergence of Cech persistence diagrams for samplings of submanifolds}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94624},
}

@inproceedings{icml2026_gromov_51717,
  title = {{Gromov-Wasserstein at Scale, Beyond Squared Norms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=P9LofJB8fs},
}

@inproceedings{icml2026_quantifying_54181,
  title = {{Quantifying the noise sensitivity of the Wasserstein metric for images}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=mh2eIxPKMm},
}

@inproceedings{iclr2025_linear_81181,
  title = {{Linear Partial Gromov-Wasserstein Embedding}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30589},
}

@inproceedings{iclr2025_fourier_81208,
  title = {{Fourier Sliced-Wasserstein Embedding for Multisets and Measures}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30562},
}

@inproceedings{iclr2026_on_86290,
  title = {{On the Wasserstein Geodesic Principal Component Analysis of probability measures}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=OJupg4mDjS},
}

@inproceedings{neurips2026_beyond_89767,
  title = {{Beyond Kemeny Medians: Consensus Ranking Distributions. Definition, Properties and Statistical Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/149736},
}

@inproceedings{neurips2026_fr_90817,
  title = {{Fréchet Regression on the Bures-Wasserstein Manifold}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/155894},
}

@inproceedings{neurips2026_a_94647,
  title = {{A Unified Approach for Computing Wasserstein Barycenters of Discrete and Continuous Measures}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/154277},
}

@inproceedings{aaai2026_on_107017,
  title = {{On the Information Processing of One-Dimensional Wasserstein Distances with Finite Samples}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39369},
}

@inproceedings{icml2025_finding_47375,
  title = {{Finding Wasserstein Ball Center: Efficient Algorithm and The Applications in Fairness}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45217},
}

@inproceedings{icml2026_achieving_51730,
  title = {{Achieving Structurally Robust Gromov Wasserstein Distance via Adaptive Dual-Mask}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=PK0zeaGAJ9},
}

@inproceedings{icml2026_lobcd_53283,
  title = {{LoBCD-GW: A Fast and Data-Dependent Algorithm for Computing Gromov-Wasserstein Distance via Localized Block Coordinate Descent}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=dxwRakfEFm},
}

@inproceedings{iclr2026_persistence_87689,
  title = {{PERSISTENCE SPHERES: BI-CONTINUOUS REPRESENTATIONS OF PERSISTENCE DIAGRAMS.}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=eITU6vjnIa},
}

@inproceedings{iclr2026_efficient_89255,
  title = {{Efficient algorithms for Incremental Metric Bipartite Matching}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=wnIanx0r0w},
}

@inproceedings{neurips2026_quantifying_92938,
  title = {{Quantifying Centrality for Complex Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/150293},
}