Bibliography

Please cite this library and the related papers when using them in scientific publications.

@article{multipers,
  title = {Multipers: {{Multiparameter Persistence}} for {{Machine Learning}}},
  shorttitle = {Multipers},
  author = {Loiseaux, David and Schreiber, Hannah},
  year = {2024},
  month = nov,
  journal = {Journal of Open Source Software},
  volume = {9},
  number = {103},
  pages = {6773},
  issn = {2475-9066},
  doi = {10.21105/joss.06773},
  langid = {english},
}

[1]

Ángel Javier Alonso, Michael Kerber, Tung Lam, and Michael Lesnick. Delaunay bifiltrations of functions on point clouds. In Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 4872–4891. 2024. arXiv:https://epubs.siam.org/doi/pdf/10.1137/1.9781611977912.173, doi:10.1137/1.9781611977912.173.

[2]

Ángel Javier Alonso, Michael Kerber, and Siddharth Pritam. Filtration-domination in bifiltered graphs. In 2023 Proceedings of the Symposium on Algorithm Engineering and Experiments (ALENEX), pages 27–38. 2023. arXiv:https://epubs.siam.org/doi/pdf/10.1137/1.9781611977561.ch3, doi:10.1137/1.9781611977561.ch3.

[3]

Nello Blaser, Morten Brun, Odin Hoff Gardaa, and Lars M. Salbu. Core Bifiltration. June 2024. arXiv:2405.01214, doi:10.48550/arXiv.2405.01214.

[4]

Michael Kerber and Alexander Rolle. Fast minimal presentations of bi-graded persistence modules. In 2021 Proceedings of the Symposium on Algorithm Engineering and Experiments (ALENEX), pages 207–220. 2021. arXiv:https://epubs.siam.org/doi/pdf/10.1137/1.9781611976472.16, doi:10.1137/1.9781611976472.16.

[5]

David Loiseaux, Mathieu Carrière, and Andrew Blumberg. A Framework for Fast and Stable Representations of Multiparameter Persistent Homology Decompositions. Advances in Neural Information Processing Systems, 36:35774–35798, December 2023.

[6]

David Loiseaux, Mathieu Carrière, and Andrew J. Blumberg. Multi-parameter Module Approximation: an efficient and interpretable invariant for multi-parameter persistence modules with guarantees. Journal of Applied and Computational Topology, 9(4):26, December 2025. doi:10.1007/s41468-025-00222-y.

[7]

David Loiseaux and Hannah Schreiber. Multipers: Multiparameter Persistence for Machine Learning. Journal of Open Source Software, 9(103):6773, November 2024. doi:10.21105/joss.06773.

[8]

David Loiseaux, Luis Scoccola, Mathieu Carrière, Magnus Bakke Botnan, and Steve Oudot. Stable Vectorization of Multiparameter Persistent Homology using Signed Barcodes as Measures. Advances in Neural Information Processing Systems, 36:68316–68342, December 2023.

[9]

Luis Scoccola, Siddharth Setlur, David Loiseaux, Mathieu Carrière, and Steve Oudot. Differentiability and Optimization of Multiparameter Persistent Homology. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, 43986–44011. PMLR, July 2024.