% Key papers for k-means++ seeding analysis (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2016_approximate_105087,
  title = {{Approximate K-Means++ in Sublinear Time}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10259},
}

@inproceedings{neurips2019_q_59218,
  title = {{q-means: A quantum algorithm for unsupervised machine learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13572},
}

@inproceedings{neurips2017_approximation_56161,
  title = {{Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9094},
}

@inproceedings{aaai2017_compressed_104475,
  title = {{Compressed K-Means for Large-Scale Clustering}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10852},
}

@inproceedings{aaai2017_balanced_104500,
  title = {{Balanced Clustering with Least Square Regression}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10877},
}

@inproceedings{icml2016_fast_35841,
  title = {{Fast k-means with accurate bounds}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/newling16.html},
}

@inproceedings{icml2019_power_37661,
  title = {{Power k-Means Clustering}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4117},
}

@inproceedings{aaai2017_bilateral_104483,
  title = {{Bilateral k-Means Algorithm for Fast Co-Clustering}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10860},
}

@inproceedings{icml2016_speeding_35984,
  title = {{Speeding up k-means by approximating Euclidean distances via block vectors}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/bottesch16.html},
}

@inproceedings{neurips2017_sparse_56677,
  title = {{Sparse Embedded \$k\$-Means Clustering}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9116},
}

@inproceedings{aaai2022_how_2208,
  title = {{How to Find a Good Explanation for Clustering?}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/03904-how-to-find-a-good-explanation-for-clustering/},
}

@inproceedings{neurips2016_a_55531,
  title = {{A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7057},
}

@inproceedings{icml2019_a_37090,
  title = {{A Better k-means++ Algorithm via Local Search}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4015},
}

@inproceedings{icml2020_explainable_38182,
  title = {{Explainable k-Means and k-Medians Clustering}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6284},
}

@inproceedings{neurips2016_robust_55968,
  title = {{Robust k-means: a Theoretical Revisit}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6936},
}

@inproceedings{icml2017_distributed_36161,
  title = {{Distributed and Provably Good Seedings for k-Means in Constant Rounds}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/472},
}

@inproceedings{aaai2018_differential_105766,
  title = {{Differential Performance Debugging With Discriminant Regression Trees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11875},
}

@inproceedings{neurips2017_clustering_56193,
  title = {{Clustering Stable Instances of Euclidean k-means.}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9419},
}

@inproceedings{icml2021_near_39646,
  title = {{Near-Optimal Algorithms for Explainable k-Medians and k-Means}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9189},
}

@inproceedings{neurips2018_a_56804,
  title = {{A Practical Algorithm for Distributed Clustering and Outlier Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11235},
}

@inproceedings{aaai2024_big_5298,
  title = {{Big Learning Expectation Maximization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29050},
}

@inproceedings{icml2024_data_44586,
  title = {{Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33866},
}

@inproceedings{iclr2024_explaining_33996,
  title = {{Explaining Kernel Clustering via Decision Trees}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19079},
}

@inproceedings{icml2025_modified_48929,
  title = {{Modified K-means Algorithm with Local Optimality Guarantees}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43694},
}

@inproceedings{icml2026_the_49625,
  title = {{The Catastrophic Failure of *the* k-Means Algorithm in High Dimensions, and How Hartigan's Algorithm Avoids It}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=4CwO8At8Hw},
}

@inproceedings{icml2026_fast_54080,
  title = {{Fast k-means Seeding Under The Manifold Hypothesis}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=lgsMOTIAYu},
}

@inproceedings{neurips2024_approximately_71753,
  title = {{Approximately Pareto-optimal Solutions for Bi-Objective k-Clustering}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95536},
}

@inproceedings{neurips2025_a_74972,
  title = {{A Beyond-Worst-Case Analysis of Greedy k-means++}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=2ym6uVbgMN},
}

@inproceedings{iclr2025_quantum_83751,
  title = {{Quantum (Inspired) \$D^2\$-sampling with Applications}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28068},
}

@inproceedings{aaai2026_sample_106834,
  title = {{Sample-and-Search: An Effective Algorithm for Learning-Augmented k-Median Clustering in High Dimensions}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39186},
}

@inproceedings{icml2024_near_43861,
  title = {{Near-Linear Time Approximation Algorithms for k-means with Outliers}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34591},
}

@inproceedings{icml2024_analyzing_44802,
  title = {{Analyzing \$D^alpha\$ seeding for \$k\$-means}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33650},
}

@inproceedings{iclr2025_online_81889,
  title = {{Online Clustering with Nearly Optimal Consistency}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29895},
}

@inproceedings{iclr2025_new_82511,
  title = {{New Algorithms for the Learning-Augmented k-means Problem}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29284},
}

@inproceedings{iclr2026_angle_85198,
  title = {{Angle K-Means}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=BdhQWT0y9s},
}

@inproceedings{neurips2026_leaner_94933,
  title = {{Leaner transformers can easily learn to cluster}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/151648},
}

@inproceedings{neurips2026_a_95286,
  title = {{A Batched Hartigan's \$k\$-Means Clustering}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/148581},
}