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Books and Theses
- 2022
[b1]David Saulpic:
Approximation Algorithms and Sketches for Clustering. (Algorithmes d'approximation et sketches pour les problèmes de clustering). Sorbonne University, Paris, France, 2022
Journal Articles
- 2024
- [j3]Andrew Draganov
, David Saulpic, Chris Schwiegelshohn:
Settling Time vs. Accuracy Tradeoffs for Clustering Big Data. Proc. ACM Manag. Data 2(3): 173 (2024) - 2021
- [j2]Vincent Cohen-Addad, Andreas Emil Feldmann, David Saulpic:
Near-linear Time Approximation Schemes for Clustering in Doubling Metrics. J. ACM 68(6): 44:1-44:34 (2021) - [j1]Andreas Emil Feldmann, David Saulpic:
Polynomial time approximation schemes for clustering in low highway dimension graphs. J. Comput. Syst. Sci. 122: 72-93 (2021)
Conference and Workshop Papers
- 2024
- [c21]Monika Henzinger, David Saulpic, Leonhard Sidl:
Experimental Evaluation of Fully Dynamic k-Means via Coresets. ALENEX 2024: 220-233 - [c20]Max Dupré la Tour, Monika Henzinger, David Saulpic:
Fully Dynamic k-Means Coreset in Near-Optimal Update Time. ESA 2024: 100:1-100:16 - [c19]Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni, David Saulpic, David P. Woodruff, Michael Wunder:
Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond. ICML 2024 - [c18]Max Dupré la Tour, Monika Henzinger, David Saulpic:
Making Old Things New: A Unified Algorithm for Differentially Private Clustering. ICML 2024 - 2023
- [c17]Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation. FOCS 2023: 1105-1130 - 2022
- [c16]Vincent Cohen-Addad, Frederik Mallmann-Trenn, David Saulpic:
Community Recovery in the Degree-Heterogeneous Stochastic Block Model. COLT 2022: 1662-1692 - [c15]Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab Mirrokni, Andres Muñoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii:
Scalable Differentially Private Clustering via Hierarchically Separated Trees. KDD 2022: 221-230 - [c14]Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar:
Improved Coresets for Euclidean k-Means. NeurIPS 2022 - [c13]Vincent Cohen-Addad, Frederik Mallmann-Trenn, David Saulpic:
A Massively Parallel Modularity-Maximizing Algorithm with Provable Guarantees. PODC 2022: 356-365 - [c12]Vincent Cohen-Addad, Anupam Gupta, Lunjia Hu, Hoon Oh, David Saulpic:
An Improved Local Search Algorithm for k-Median. SODA 2022: 1556-1612 - [c11]Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn:
Towards optimal lower bounds for k-median and k-means coresets. STOC 2022: 1038-1051 - 2021
- [c10]Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces. NeurIPS 2021: 21085-21098 - [c9]Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
A new coreset framework for clustering. STOC 2021: 169-182 - 2020
- [c8]Andreas Emil Feldmann, David Saulpic:
Polynomial Time Approximation Schemes for Clustering in Low Highway Dimension Graphs. ESA 2020: 46:1-46:22 - [c7]Vincent Cohen-Addad, Adrian Kosowski, Frederik Mallmann-Trenn, David Saulpic:
On the Power of Louvain in the Stochastic Block Model. NeurIPS 2020 - 2019
- [c6]David Saulpic, Vincent Cohen-Addad, Andreas Emil Feldmann:
Near-Linear Time Approximations Schemes for Clustering in Doubling Metrics. FOCS 2019: 540-559 - [c5]Vincent Cohen-Addad, Niklas Hjuler, Nikos Parotsidis, David Saulpic, Chris Schwiegelshohn:
Fully Dynamic Consistent Facility Location. NeurIPS 2019: 3250-3260 - [c4]Niklas Hjuler, Giuseppe F. Italiano, Nikos Parotsidis, David Saulpic:
Dominating Sets and Connected Dominating Sets in Dynamic Graphs. STACS 2019: 35:1-35:17 - 2018
- [c3]Amariah Becker, Philip N. Klein, David Saulpic:
Polynomial-Time Approximation Schemes for k-center, k-median, and Capacitated Vehicle Routing in Bounded Highway Dimension. ESA 2018: 8:1-8:15 - 2017
- [c2]Amariah Becker, Philip N. Klein, David Saulpic:
A Quasi-Polynomial-Time Approximation Scheme for Vehicle Routing on Planar and Bounded-Genus Graphs. ESA 2017: 12:1-12:15 - [c1]Rémi Géraud, Mirko Koscina, Paul Lenczner, David Naccache, David Saulpic:
Generating Functionally Equivalent Programs Having Non-isomorphic Control-Flow Graphs. NordSec 2017: 265-279
Informal and Other Publications
- 2024
- [i19]Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni, David Saulpic, David P. Woodruff, Michael Wunder:
Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond. CoRR abs/2402.17327 (2024) - [i18]Andrew Draganov, David Saulpic, Chris Schwiegelshohn:
Settling Time vs. Accuracy Tradeoffs for Clustering Big Data. CoRR abs/2404.01936 (2024) - [i17]Nikhil Bansal, Vincent Cohen-Addad, Milind Prabhu, David Saulpic, Chris Schwiegelshohn:
Sensitivity Sampling for k-Means: Worst Case and Stability Optimal Coreset Bounds. CoRR abs/2405.01339 (2024) - [i16]Max Dupré la Tour, Monika Henzinger, David Saulpic:
Making Old Things New: A Unified Algorithm for Differentially Private Clustering. CoRR abs/2406.11649 (2024) - [i15]Max Dupré la Tour, Monika Henzinger, David Saulpic:
Fully Dynamic k-Means Coreset in Near-Optimal Update Time. CoRR abs/2406.19926 (2024) - [i14]Max Dupré la Tour, David Saulpic:
Almost-linear Time Approximation Algorithm to Euclidean k-median and k-means. CoRR abs/2407.11217 (2024) - 2023
- [i13]Max Dupré la Tour, Monika Henzinger, David Saulpic:
Differential Privacy for Clustering Under Continual Observation. CoRR abs/2307.03430 (2023) - [i12]Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation. CoRR abs/2310.04076 (2023) - [i11]Monika Henzinger, David Saulpic, Leonhard Sidl:
Experimental Evaluation of Fully Dynamic k-Means via Coresets. CoRR abs/2310.18034 (2023) - 2022
- [i10]Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn:
Towards Optimal Lower Bounds for k-median and k-means Coresets. CoRR abs/2202.12793 (2022) - [i9]Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab S. Mirrokni, Andres Muñoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii:
Scalable Differentially Private Clustering via Hierarchically Separated Trees. CoRR abs/2206.08646 (2022) - [i8]Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar:
Improved Coresets for Euclidean k-Means. CoRR abs/2211.08184 (2022) - 2021
- [i7]Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
A New Coreset Framework for Clustering. CoRR abs/2104.06133 (2021) - [i6]Vincent Cohen-Addad, Anupam Gupta, Lunjia Hu, Hoon Oh, David Saulpic:
An Improved Local Search Algorithm for k-Median. CoRR abs/2111.04589 (2021) - 2020
- [i5]Andreas Emil Feldmann, David Saulpic:
Polynomial Time Approximation Schemes for Clustering in Low Highway Dimension Graphs. CoRR abs/2006.12897 (2020) - 2019
- [i4]Niklas Hjuler, Giuseppe F. Italiano, Nikos Parotsidis, David Saulpic:
Dominating Sets and Connected Dominating Sets in Dynamic Graphs. CoRR abs/1901.09877 (2019) - 2018
- [i3]Vincent Cohen-Addad, Andreas Emil Feldmann, David Saulpic:
Near-Linear Time Approximation Schemes for Clustering in Doubling Metrics. CoRR abs/1812.08664 (2018) - 2017
- [i2]Amariah Becker, Philip N. Klein, David Saulpic:
Polynomial-Time Approximation Schemes for k-Center and Bounded-Capacity Vehicle Routing in Metrics with Bounded Highway Dimension. CoRR abs/1707.08270 (2017) - [i1]Rémi Géraud, Mirko Koscina, Paul Lenczner, David Naccache, David Saulpic:
Generating Functionally Equivalent Programs Having Non-Isomorphic Control-Flow Graphs. CoRR abs/1709.08357 (2017)
Coauthor Index
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