Cohen-Addad, Vincent

39 publications

ICML 2025 Algorithms and Hardness for Active Learning on Graphs Vincent Cohen-Addad, Silvio Lattanzi, Simon Meierhans

ICML 2025 Correlation Clustering Beyond the Pivot Algorithm Soheil Behnezhad, Moses Charikar, Vincent Cohen-Addad, Alma Ghafari, Weiyun Ma

NeurIPS 2025 Efficient Data Selection at Scale via Influence Distillation Mahdi Nikdan, Vincent Cohen-Addad, Dan Alistarh, Vahab Mirrokni

ICLR 2025 Fair Clustering in the Sliding Window Model Vincent Cohen-Addad, Shaofeng H.-C. Jiang, Qiaoyuan Yang, Yubo Zhang, Samson Zhou

COLT 2025 Metric Embeddings Beyond Bi-Lipschitz Distortion via Sherali-Adams Ainesh Bakshi, Vincent Cohen-Addad, Rajesh Jayaram, Samuel B. Hopkins, Silvio Lattanzi

ICML 2025 REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Vincent Cohen-Addad, Johannes Gasteiger, Stephan Günnemann

ICML 2025 Scalable Private Partition Selection via Adaptive Weighting Justin Y. Chen, Vincent Cohen-Addad, Alessandro Epasto, Morteza Zadimoghaddam

ICML 2025 The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence Tom Wollschläger, Jannes Elstner, Simon Geisler, Vincent Cohen-Addad, Stephan Günnemann, Johannes Gasteiger

ICML 2024 A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering Vincent Cohen-Addad, Tommaso D’Orsi, Aida Mousavifar

AISTATS 2024 A Scalable Algorithm for Individually Fair K-Means Clustering MohammadHossein Bateni, Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi

ICML 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 Woodruff, Michael Wunder

ICML 2024 Dynamic Correlation Clustering in Sublinear Update Time Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis

NeurIPS 2024 Learning-Augmented Approximation Algorithms for Maximum Cut and Related Problems Vincent Cohen-Addad, Tommaso d'Orsi, Anupam Gupta, Euiwoong Lee, Debmalya Panigrahi

ICML 2024 Multi-View Stochastic Block Models Vincent Cohen-Addad, Tommaso D’Orsi, Silvio Lattanzi, Rajai Nasser

ICML 2024 Perturb-and-Project: Differentially Private Similarities and Marginals Vincent Cohen-Addad, Tommaso D’Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong

ICML 2023 Differentially Private Hierarchical Clustering with Provable Approximation Guarantees Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni

NeurIPS 2023 Multi-Swap K-Means++ Lorenzo Beretta, Vincent Cohen-Addad, Silvio Lattanzi, Nikos Parotsidis

NeurIPS 2023 Private Estimation Algorithms for Stochastic Block Models and Mixture Models Hongjie Chen, Vincent Cohen-Addad, Tommaso d’Orsi, Alessandro Epasto, Jacob Imola, David Steurer, Stefan Tiegel

AISTATS 2022 On Facility Location Problem in the Local Differential Privacy Model Vincent Cohen-Addad, Yunus Esencayi, Chenglin Fan, Marco Gaboradi, Shi Li, Di Wang

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

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

ICML 2022 Massively Parallel $k$-Means Clustering for Perturbation Resilient Instances Vincent Cohen-Addad, Vahab Mirrokni, Peilin Zhong

NeurIPS 2022 Near-Optimal Correlation Clustering with Privacy Vincent Cohen-Addad, Chenglin Fan, Silvio Lattanzi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub M Tarnawski

NeurIPS 2022 Near-Optimal Private and Scalable $k$-Clustering Vincent Cohen-Addad, Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong

ICML 2022 Online and Consistent Correlation Clustering Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis

NeurIPSW 2022 Scalable and Improved Algorithms for Individually Fair Clustering Mohammadhossein Bateni, Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi

AISTATS 2021 Online K-Means Clustering Vincent Cohen-Addad, Benjamin Guedj, Varun Kanade, Guy Rom

ICML 2021 Correlation Clustering in Constant Many Parallel Rounds Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski

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

ICML 2021 Improving Ultrametrics Embeddings Through Coresets Vincent Cohen-Addad, Rémi De Joannis De Verclos, Guillaume Lagarde

NeurIPS 2021 Parallel and Efficient Hierarchical K-Median Clustering Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, Ola Svensson

NeurIPS 2020 Fast and Accurate $k$-Means++ via Rejection Sampling Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, Ola Svensson

ICML 2020 On Efficient Low Distortion Ultrametric Embedding Vincent Cohen-Addad, C. S. Karthik, Guillaume Lagarde

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

NeurIPS 2019 Fully Dynamic Consistent Facility Location Vincent Cohen-Addad, Niklas Oskar D Hjuler, Nikos Parotsidis, David Saulpic, Chris Schwiegelshohn

NeurIPS 2019 Subquadratic High-Dimensional Hierarchical Clustering Amir Abboud, Vincent Cohen-Addad, Hussein Houdrouge

NeurIPS 2018 Clustering Redemption–Beyond the Impossibility of Kleinberg’s Axioms Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn

NeurIPS 2017 Hierarchical Clustering Beyond the Worst-Case Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn

AISTATS 2017 Online Optimization of Smoothed Piecewise Constant Functions Vincent Cohen-Addad, Varun Kanade