A Quasi-Polynomial-Time Approximation Scheme for Vehicle Routing on Planar and Bounded-Genus Graphs. - Amariah Becker; Philip N. Klein; David Saulpic (ESA, 2017)

Generating Functionally Equivalent Programs Having Non-isomorphic Control-Flow Graphs. - Rémi Géraud; Mirko Koscina; Paul Lenczner; David Naccache; David Saulpic (NordSec, 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)

Polynomial-Time Approximation Schemes for k-center, k-median, and Capacitated Vehicle Routing in Bounded Highway Dimension. - Amariah Becker; Philip N. Klein; David Saulpic (ESA, 2018)

Near-Linear Time Approximations Schemes for Clustering in Doubling Metrics. - David Saulpic; Vincent Cohen-Addad; Andreas Emil Feldmann (FOCS, 2019)

Fully Dynamic Consistent Facility Location. - Vincent Cohen-Addad; Niklas Hjuler; Nikos Parotsidis; David Saulpic; Chris Schwiegelshohn (NeurIPS, 2019)

Dominating Sets and Connected Dominating Sets in Dynamic Graphs. - Niklas Hjuler; Giuseppe F. Italiano; Nikos Parotsidis; David Saulpic (STACS, 2019)

Polynomial Time Approximation Schemes for Clustering in Low Highway Dimension Graphs. - Andreas Emil Feldmann; David Saulpic (ESA, 2020)

On the Power of Louvain in the Stochastic Block Model. - Vincent Cohen-Addad; Adrian Kosowski; Frederik Mallmann-Trenn; David Saulpic (NeurIPS, 2020)

Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces. - Vincent Cohen-Addad; David Saulpic; Chris Schwiegelshohn (NeurIPS, 2021)

A new coreset framework for clustering. - Vincent Cohen-Addad; David Saulpic; Chris Schwiegelshohn (STOC, 2021)

Near-linear Time Approximation Schemes for Clustering in Doubling Metrics. - Vincent Cohen-Addad; Andreas Emil Feldmann; David Saulpic (J. ACM, 2021)

Community Recovery in the Degree-Heterogeneous Stochastic Block Model. - Vincent Cohen-Addad; Frederik Mallmann-Trenn; David Saulpic (COLT, 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 (KDD, 2022)

Improved Coresets for Euclidean k-Means. - Vincent Cohen-Addad; Kasper Green Larsen; David Saulpic; Chris Schwiegelshohn; Omar Ali Sheikh-Omar (NeurIPS, 2022)

A Massively Parallel Modularity-Maximizing Algorithm with Provable Guarantees. - Vincent Cohen-Addad; Frederik Mallmann-Trenn; David Saulpic (PODC, 2022)

An Improved Local Search Algorithm for k-Median. - Vincent Cohen-Addad; Anupam Gupta 001; Lunjia Hu; Hoon Oh; David Saulpic (SODA, 2022)

Towards optimal lower bounds for k-median and k-means coresets. - Vincent Cohen-Addad; Kasper Green Larsen; David Saulpic; Chris Schwiegelshohn (STOC, 2022)

Approximation Algorithms and Sketches for Clustering. (Algorithmes d'approximation et sketches pour les problèmes de clustering). - David Saulpic (2022)

Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation. - Vincent Cohen-Addad; David Saulpic; Chris Schwiegelshohn (FOCS, 2023)

Differential Privacy for Clustering Under Continual Observation. - Max Dupré la Tour; Monika Henzinger; David Saulpic (CoRR, 2023)

Experimental Evaluation of Fully Dynamic k-Means via Coresets. - Monika Henzinger; David Saulpic; Leonhard Sidl (CoRR, 2023)

Experimental Evaluation of Fully Dynamic k-Means via Coresets. - Monika Henzinger; David Saulpic; Leonhard Sidl (ALENEX, 2024)

Fully Dynamic k-Means Coreset in Near-Optimal Update Time. - Max Dupré la Tour; Monika Henzinger; David Saulpic (ESA, 2024)

Sensitivity Sampling for k-Means: Worst Case and Stability Optimal Coreset Bounds. - Nikhil Bansal 001; Vincent Cohen-Addad; Milind Prabhu; David Saulpic; Chris Schwiegelshohn (FOCS, 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 (ICML, 2024)

Making Old Things New: A Unified Algorithm for Differentially Private Clustering. - Max Dupré la Tour; Monika Henzinger; David Saulpic (ICML, 2024)

Almost-linear Time Approximation Algorithm to Euclidean k-median and k-means. - Max Dupré la Tour; David Saulpic (CoRR, 2024)

Settling Time vs. Accuracy Tradeoffs for Clustering Big Data. - Andrew Draganov; David Saulpic; Chris Schwiegelshohn (Proc. ACM Manag. Data, 2024)

Estimating the Electoral Consequences of Legislative Redistricting in France. - Evripidis Bampis; Thomas Ehrhard; Bruno Escoffier; Claire Mathieu; Fanny Pascual; David Saulpic (FAccT, 2025)

A Tight VC-Dimension Analysis of Clustering Coresets with Applications. - Vincent Cohen-Addad; Andrew Draganov; Matteo Russo 002; David Saulpic; Chris Schwiegelshohn (SODA, 2025)

Differentially Private Federated k-Means Clustering with Server-Side Data. - Jonathan Scott; Christoph H. Lampert; David Saulpic (CoRR, 2025)