David Saulpic
5 Publications
2024 | Published | Conference Paper | IST-REx-ID: 18308 | 
M. D. La Tour, M. Henzinger, and D. Saulpic, “Fully dynamic k-means coreset in near-optimal update time,” in 32nd Annual European Symposium on Algorithms, London, United Kingdom, 2024, vol. 308.
[Published Version]
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18115 |
K. Axiotis et al., “Data-efficient learning via clustering-based sensitivity sampling: Foundation models and beyond,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 2086–2107.
[Published Version]
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 14769 |
M. Henzinger, D. Saulpic, and L. Sidl, “Experimental evaluation of fully dynamic k-means via coresets,” in 2024 Proceedings of the Symposium on Algorithm Engineering and Experiments, Alexandria, VA, United States, 2024, pp. 220–233.
[Preprint]
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18116 |
M. D. La Tour, M. Henzinger, and D. Saulpic, “Making old things new: A unified algorithm for differentially private clustering,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 12046–12086.
[Published Version]
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14768 |
V. Cohen-Addad, D. Saulpic, and C. Schwiegelshohn, “Deterministic clustering in high dimensional spaces: Sketches and approximation,” in 2023 IEEE 64th Annual Symposium on Foundations of Computer Science, Santa Cruz, CA, United States, 2023, pp. 1105–1130.
[Preprint]
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| Download Preprint (ext.)
| WoS
| arXiv
Grants
5 Publications
2024 | Published | Conference Paper | IST-REx-ID: 18308 |
M. D. La Tour, M. Henzinger, and D. Saulpic, “Fully dynamic k-means coreset in near-optimal update time,” in 32nd Annual European Symposium on Algorithms, London, United Kingdom, 2024, vol. 308.
[Published Version]
View
| Files available
| DOI
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18115 |
K. Axiotis et al., “Data-efficient learning via clustering-based sensitivity sampling: Foundation models and beyond,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 2086–2107.
[Published Version]
View
| Download Published Version (ext.)
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 14769 |
M. Henzinger, D. Saulpic, and L. Sidl, “Experimental evaluation of fully dynamic k-means via coresets,” in 2024 Proceedings of the Symposium on Algorithm Engineering and Experiments, Alexandria, VA, United States, 2024, pp. 220–233.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18116 |
M. D. La Tour, M. Henzinger, and D. Saulpic, “Making old things new: A unified algorithm for differentially private clustering,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 12046–12086.
[Published Version]
View
| Download Published Version (ext.)
| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14768 |
V. Cohen-Addad, D. Saulpic, and C. Schwiegelshohn, “Deterministic clustering in high dimensional spaces: Sketches and approximation,” in 2023 IEEE 64th Annual Symposium on Foundations of Computer Science, Santa Cruz, CA, United States, 2023, pp. 1105–1130.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv