| 2026 | ICALP | Faster and Simpler Greedy Algorithm for k-Median and k-Means. | Max Dupr la Tour, David Saulpic |
| 2025 | ICML | Differentially Private Federated k-Means Clustering with Server-Side Data. | Jonathan Scott, Christoph H. Lampert, David Saulpic |
| 2025 | SODA | A Tight VC-Dimension Analysis of Clustering Coresets with Applications. | Vincent Cohen-Addad, Andrew Draganov, Matteo Russo, David Saulpic, Chris Schwiegelshohn |
| 2024 | ALENEX | Experimental Evaluation of Fully Dynamic | Monika Henzinger, David Saulpic, Leonhard Sidl |
| 2024 | ESA | Fully Dynamic k-Means Coreset in Near-Optimal Update Time. | Max Dupr la Tour, Monika Henzinger, David Saulpic |
| 2024 | FOCS | Sensitivity Sampling for k-Means: Worst Case and Stability Optimal Coreset Bounds. | Nikhil Bansal, Vincent Cohen-Addad, Milind Prabhu, David Saulpic, Chris Schwiegelshohn |
| 2024 | ICML | 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 |
| 2024 | ICML | Making Old Things New: A Unified Algorithm for Differentially Private Clustering. | Max Dupr la Tour, Monika Henzinger, David Saulpic |
| 2023 | FOCS | Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation. | Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn |
| 2022 | COLT | Community Recovery in the Degree-Heterogeneous Stochastic Block Model. | Vincent Cohen-Addad, Frederik Mallmann-Trenn, David Saulpic |
| 2022 | KDD | Scalable Differentially Private Clustering via Hierarchically Separated Trees. | Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab Mirrokni, Andres Muoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii |
| 2022 | PODC | A Massively Parallel Modularity-Maximizing Algorithm with Provable Guarantees. | Vincent Cohen-Addad, Frederik Mallmann-Trenn, David Saulpic |
| 2022 | SODA | An Improved Local Search Algorithm for k-Median. | Vincent Cohen-Addad, Anupam Gupta, Lunjia Hu, Hoon Oh, David Saulpic |
| 2022 | STOC | Towards optimal lower bounds for k-median and k-means coresets. | Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn |
| 2021 | STOC | A new coreset framework for clustering. | Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn |
| 2020 | ESA | Polynomial Time Approximation Schemes for Clustering in Low Highway Dimension Graphs. | Andreas Emil Feldmann, David Saulpic |
| 2019 | FOCS | Near-Linear Time Approximations Schemes for Clustering in Doubling Metrics. | David Saulpic, Vincent Cohen-Addad, Andreas Emil Feldmann |
| 2019 | STACS | Dominating Sets and Connected Dominating Sets in Dynamic Graphs. | Niklas Hjuler, Giuseppe F. Italiano, Nikos Parotsidis, David Saulpic |
| 2018 | ESA | Polynomial-Time Approximation Schemes for k-center, k-median, and Capacitated Vehicle Routing in Bounded Highway Dimension. | Amariah Becker, Philip N. Klein, David Saulpic |
| 2017 | ESA | A Quasi-Polynomial-Time Approximation Scheme for Vehicle Routing on Planar and Bounded-Genus Graphs. | Amariah Becker, Philip N. Klein, David Saulpic |