V. Cohen‑Addad, A. Epasto, S. Lattanzi, V. Mirrokni, A. Munoz Medina, D. Saulpic, Ch. Schwiegelshohn, S. Vassilvitskii : “Scalable Differentially Private Clustering via Hierarchically Separated Trees”, KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington DC, United States, pp. 221-230, (ACM), (ISBN: 978-1-4503-9385-0) (2022)
V. Cohen‑Addad, K. Larsen, D. Saulpic, Ch. Schwiegelshohn : “Towards Optimal Lower Bounds for k-median and k-means Coresets”, Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2022, Rome, Italy, pp. 1038-1051, (Association for Computing Machinery), (ISBN: 9781450392648) (2022)
V. Cohen‑Addad, A. Gupta, L. Hu, H. Oh, D. Saulpic : “An Improved Local Search Algorithm for k-Median”, ACM-SIAM Symposium on Discrete Algorithms (SODA22), Alexandria (virtual event), VA, United States, pp. 1556-1612, (Society for Industrial and Applied Mathematics), (ISBN: 978-1-61197-707-3) (2022)
V. Cohen‑Addad, D. Saulpic, Ch. Schwiegelshohn : “A new coreset framework for clustering”, STOC 2021: Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing, Rome ( Virtual ), Italy, pp. 169-182, (ACM) (2021)
V. Cohen‑Addad, B. Guedj, V. Kanade, G. Rom : “Online $k$-means Clustering”, AISTATS 2021 - The 24th International Conference on Artificial Intelligence and Statistics, Virtual, France (2021)
V. Cohen‑Addad, F. Mallmann‑Trenn, C. Mathieu : “Instance-Optimality in the Noisy Value-and Comparison-Model”, Proceedings of the 2020 {ACM-SIAM} Symposium on Discrete Algorithms, {SODA} 2020, Salt Lake City, United States, pp. 2124-2143, (Society for Industrial and Applied Mathematics) (2020)
2019
V. Cohen‑Addad, N. Hjuler, N. Parotsidis, D. Saulpic, Ch. Schwiegelshohn : “Fully Dynamic Consistent Facility Location”, NeurIPS'19 - 33rd Conference on Neural Information Processing Systems, Vancouver, United States (2019)
V. Cohen‑Addad, K. Srikanta : “Inapproximability of Clustering in Lp-metrics”, FOCS'19 - 60th Annual IEEE Symposium on Foundations of Computer Science, Baltimore, United States (2019)
V. Cohen‑Addad, V. Kanade, F. Mallmann‑Trenn, C. Mathieu : “Hierarchical Clustering”, Journal of the ACM (JACM), vol. 66 (4), pp. 1-42, (Association for Computing Machinery) (2019)
V. Cohen‑Addad, A. Gupta, A. Kumar, E. Lee, J. Li : “Tight FPT Approximations for k-Median and k-Means”, 46th International Colloquium on Automata, Languages, and Programming (ICALP 2019), vol. 132, Leibniz International Proceedings in Informatics, Patras, Greece, pp. 42:1-42:14, (Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik) (2019)
V. Cohen‑Addad, J. Li : “On the Fixed-Parameter Tractability of Capacitated Clustering”, 46th International Colloquium on Automata, Languages, and Programming (ICALP 2019), vol. 132, Leibniz International Proceedings in Informatics (LIPIcs), Patras, Greece, pp. 41:1-41:14, (Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik) (2019)
M. Abrahamsen, A. Adamaszek, K. Bringmann, V. Cohen‑Addad, M. Mehr, E. Rotenberg, A. Roytman, M. Thorup : “Fast fencing”, The 50th Annual ACM SIGACT Symposium on Theory of Computing, Los Angeles, United States, pp. 564-573, (ACM Press) (2018)
V. Cohen‑Addad, A. De Mesmay, E. Rotenberg, A. Roytman : “The Bane of Low-Dimensionality Clustering”, Proceedings of the Twenty-Ninth ACM-SIAM Symposium on Discrete Algorithms, New Orleans, United States (2018)
V. Cohen‑Addad : “A Fast Approximation Scheme for Low-Dimensional k-Means”, SODA 2018 - 29h Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, United States, pp. 430-440, (Society for Industrial and Applied Mathematics) (2018)
V. Cohen‑Addad, V. Kanade, F. Mallmann‑Trenn, C. Mathieu : “Hierarchical Clustering: Objective Functions and Algorithms”, SODA 2018 - Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, United States, pp. 378-397, (Society for Industrial and Applied Mathematics) (2018)