David Saulpic

According to our database, David Saulpic authored at least 23 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond.

Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni, David Saulpic, David P. Woodruff, Michael Wunder

CoRR, 2024

2023

Experimental Evaluation of Fully Dynamic k-Means via Coresets.

Monika Henzinger, David Saulpic, Leonhard Sidl

CoRR, 2023

Differential Privacy for Clustering Under Continual Observation.

Max Dupré la Tour, Monika Henzinger, David Saulpic

CoRR, 2023

Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation.

Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn

Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science, 2023

2022

Approximation Algorithms and Sketches for Clustering. (Algorithmes d'approximation et sketches pour les problèmes de clustering).

David Saulpic

PhD thesis, 2022

Towards optimal lower bounds for k-median and k-means coresets.

Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn

Proceedings of the STOC '22: 54th Annual ACM SIGACT Symposium on Theory of Computing, Rome, Italy, June 20, 2022

An Improved Local Search Algorithm for k-Median.

Vincent Cohen-Addad, Anupam Gupta, Lunjia Hu, Hoon Oh, David Saulpic

Proceedings of the 2022 ACM-SIAM Symposium on Discrete Algorithms, 2022

A Massively Parallel Modularity-Maximizing Algorithm with Provable Guarantees.

Vincent Cohen-Addad, Frederik Mallmann-Trenn, David Saulpic

Proceedings of the PODC '22: ACM Symposium on Principles of Distributed Computing, Salerno, Italy, July 25, 2022

Improved Coresets for Euclidean k-Means.

Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Scalable Differentially Private Clustering via Hierarchically Separated Trees.

Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab Mirrokni, Andres Muñoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Community Recovery in the Degree-Heterogeneous Stochastic Block Model.

Vincent Cohen-Addad, Frederik Mallmann-Trenn, David Saulpic

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Polynomial time approximation schemes for clustering in low highway dimension graphs.

Andreas Emil Feldmann, David Saulpic

J. Comput. Syst. Sci., 2021

Near-linear Time Approximation Schemes for Clustering in Doubling Metrics.

Vincent Cohen-Addad, Andreas Emil Feldmann, David Saulpic

J. ACM, 2021

A new coreset framework for clustering.

Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn

Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces.

Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

On the Power of Louvain in the Stochastic Block Model.

Vincent Cohen-Addad, Adrian Kosowski, Frederik Mallmann-Trenn, David Saulpic

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Dominating Sets and Connected Dominating Sets in Dynamic Graphs.

Niklas Hjuler, Giuseppe F. Italiano, Nikos Parotsidis, David Saulpic

Proceedings of the 36th International Symposium on Theoretical Aspects of Computer Science, 2019

Fully Dynamic Consistent Facility Location.

Vincent Cohen-Addad, Niklas Hjuler, Nikos Parotsidis, David Saulpic, Chris Schwiegelshohn

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Near-Linear Time Approximations Schemes for Clustering in Doubling Metrics.

David Saulpic, Vincent Cohen-Addad, Andreas Emil Feldmann

Proceedings of the 60th IEEE Annual Symposium on Foundations of Computer Science, 2019

2018

Polynomial-Time Approximation Schemes for k-center, k-median, and Capacitated Vehicle Routing in Bounded Highway Dimension.

Amariah Becker, Philip N. Klein, David Saulpic

Proceedings of the 26th Annual European Symposium on Algorithms, 2018

2017

Polynomial-Time Approximation Schemes for k-Center and Bounded-Capacity Vehicle Routing in Metrics with Bounded Highway Dimension.

Amariah Becker, Philip N. Klein, David Saulpic

CoRR, 2017

Generating Functionally Equivalent Programs Having Non-isomorphic Control-Flow Graphs.

Rémi Géraud, Mirko Koscina, Paul Lenczner, David Naccache, David Saulpic

Proceedings of the Secure IT Systems, 2017

A Quasi-Polynomial-Time Approximation Scheme for Vehicle Routing on Planar and Bounded-Genus Graphs.

Amariah Becker, Philip N. Klein, David Saulpic

Proceedings of the 25th Annual European Symposium on Algorithms, 2017