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ORCIDVincent Cohen-Addad
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- affiliation: Google Zurich, Switzerland
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2020 – today
- 2024
[j13]Vincent Cohen-Addad, Surya Teja Gavva, Karthik C. S., Claire Mathieu, Namrata:
Fairness of linear regression in decision making. Int. J. Data Sci. Anal. 18(3): 337-347 (2024)- [j12]Vincent Cohen-Addad
, Debarati Das, Evangelos Kipouridis, Nikos Parotsidis, Mikkel Thorup:
Fitting Distances by Tree Metrics Minimizing the Total Error within a Constant Factor. J. ACM 71(2): 10:1-10:41 (2024) - [j11]Vincent Cohen-Addad, Tobias Mömke, Victor Verdugo:
A 2-approximation for the bounded treewidth sparsest cut problem in sfFPT Time. Math. Program. 206(1): 479-495 (2024) - [c86]MohammadHossein Bateni, Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi:
A Scalable Algorithm for Individually Fair k-Means Clustering. AISTATS 2024: 3151-3159 - [c85]Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni, David Saulpic, David P. Woodruff, Michael Wunder:
Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond. ICML 2024 - [c84]Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis:
Dynamic Correlation Clustering in Sublinear Update Time. ICML 2024 - [c83]Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong:
Perturb-and-Project: Differentially Private Similarities and Marginals. ICML 2024 - [c82]Vincent Cohen-Addad, Tommaso d'Orsi, Silvio Lattanzi, Rajai Nasser:
Multi-View Stochastic Block Models. ICML 2024 - [c81]Vincent Cohen-Addad, Tommaso d'Orsi, Aida Mousavifar:
A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering. ICML 2024 - [c80]Vincent Cohen-Addad, Chenglin Fan, Suprovat Ghoshal, Euiwoong Lee, Arnaud de Mesmay, Alantha Newman, Tony Chang Wang:
A PTAS for ℓ0-Low Rank Approximation: Solving Dense CSPs over Reals. SODA 2024: 935-961 - [c79]Nairen Cao, Vincent Cohen-Addad, Euiwoong Lee, Shi Li, Alantha Newman, Lukas Vogl:
Understanding the Cluster Linear Program for Correlation Clustering. STOC 2024: 1605-1616 - [c78]Vincent Cohen-Addad, David Rasmussen Lolck, Marcin Pilipczuk, Mikkel Thorup, Shuyi Yan, Hanwen Zhang:
Combinatorial Correlation Clustering. STOC 2024: 1617-1628 - [i75]MohammadHossein Bateni, Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi:
A Scalable Algorithm for Individually Fair K-means Clustering. CoRR abs/2402.06730 (2024) - [i74]Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni, David Saulpic, David P. Woodruff, Michael Wunder:
Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond. CoRR abs/2402.17327 (2024) - [i73]Vincent Cohen-Addad, Tommaso d'Orsi, Anupam Gupta, Euiwoong Lee, Debmalya Panigrahi:
Max-Cut with ε-Accurate Predictions. CoRR abs/2402.18263 (2024) - [i72]Vincent Cohen-Addad, David Rasmussen Lolck, Marcin Pilipczuk, Mikkel Thorup, Shuyi Yan, Hanwen Zhang:
Combinatorial Correlation Clustering. CoRR abs/2404.05433 (2024) - [i71]Soheil Behnezhad, Moses Charikar, Vincent Cohen-Addad, Alma Ghafari, Weiyun Ma:
Fully Dynamic Correlation Clustering: Breaking 3-Approximation. CoRR abs/2404.06797 (2024) - [i70]Nairen Cao, Vincent Cohen-Addad, Euiwoong Lee, Shi Li, Alantha Newman, Lukas Vogl:
Understanding the Cluster LP for Correlation Clustering. CoRR abs/2404.17509 (2024) - [i69]Nikhil Bansal, Vincent Cohen-Addad, Milind Prabhu, David Saulpic, Chris Schwiegelshohn:
Sensitivity Sampling for k-Means: Worst Case and Stability Optimal Coreset Bounds. CoRR abs/2405.01339 (2024) - [i68]Vincent Cohen-Addad, Tommaso d'Orsi, Aida Mousavifar:
A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering. CoRR abs/2406.04857 (2024) - [i67]Vincent Cohen-Addad, Tommaso d'Orsi, Silvio Lattanzi, Rajai Nasser:
Multi-View Stochastic Block Models. CoRR abs/2406.04860 (2024) - [i66]Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong:
Perturb-and-Project: Differentially Private Similarities and Marginals. CoRR abs/2406.04868 (2024) - [i65]Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis:
Dynamic Correlation Clustering in Sublinear Update Time. CoRR abs/2406.09137 (2024) - [i64]Yanfei Chen, Jinsung Yoon, Devendra Singh Sachan, Qingze Wang, Vincent Cohen-Addad, MohammadHossein Bateni, Chen-Yu Lee, Tomas Pfister:
Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval. CoRR abs/2408.01875 (2024) - 2023
- [j10]Elette Boyle, Vincent Cohen-Addad, Alexandra Kolla, Mikkel Thorup:
Special Section on the Fifty-Ninth Annual IEEE Symposium on Foundations of Computer Science (2018). SIAM J. Comput. 52(6): S18- (2023) - [j9]Karl Bringmann, Vincent Cohen-Addad, Debarati Das:
A Linear-Time n0.4-Approximation for Longest Common Subsequence. ACM Trans. Algorithms 19(1): 9:1-9:24 (2023) - [c77]Amir Abboud, MohammadHossein Bateni, Vincent Cohen-Addad, Karthik C. S., Saeed Seddighin:
On Complexity of 1-Center in Various Metrics. APPROX/RANDOM 2023: 1:1-1:19 - [c76]Vincent Cohen-Addad, David P. Woodruff, Samson Zhou:
Streaming Euclidean k-median and k-means with o(log n) Space. FOCS 2023: 883-908 - [c75]Vincent Cohen-Addad, Euiwoong Lee, Shi Li, Alantha Newman:
Handling Correlated Rounding Error via Preclustering: A 1.73-approximation for Correlation Clustering. FOCS 2023: 1082-1104 - [c74]Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation. FOCS 2023: 1105-1130 - [c73]Vincent Cohen-Addad, Hung Le, Marcin Pilipczuk, Michal Pilipczuk:
Planar and Minor-Free Metrics Embed into Metrics of Polylogarithmic Treewidth with Expected Multiplicative Distortion Arbitrarily Close to 1. FOCS 2023: 2262-2277 - [c72]Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni:
Differentially Private Hierarchical Clustering with Provable Approximation Guarantees. ICML 2023: 14353-14375 - [c71]Siddhartha Banerjee, Vincent Cohen-Addad, Anupam Gupta, Zhouzi Li:
Graph Searching with Predictions. ITCS 2023: 12:1-12:24 - [c70]Lorenzo Beretta, Vincent Cohen-Addad, Silvio Lattanzi, Nikos Parotsidis:
Multi-Swap k-Means++. NeurIPS 2023 - [c69]Hongjie Chen, Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Jacob Imola, David Steurer, Stefan Tiegel:
Private estimation algorithms for stochastic block models and mixture models. NeurIPS 2023 - [c68]Vincent Cohen-Addad, Fabrizio Grandoni, Euiwoong Lee, Chris Schwiegelshohn:
Breaching the 2 LMP Approximation Barrier for Facility Location with Applications to k-Median. SODA 2023: 940-986 - [c67]Amir Abboud, Vincent Cohen-Addad, Euiwoong Lee, Pasin Manurangsi:
On the Fine-Grained Complexity of Approximating k-Center in Sparse Graphs. SOSA 2023: 145-155 - [c66]Xi Chen, Vincent Cohen-Addad, Rajesh Jayaram, Amit Levi, Erik Waingarten:
Streaming Euclidean MST to a Constant Factor. STOC 2023: 156-169 - [i63]Hongjie Chen, Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Jacob Imola, David Steurer, Stefan Tiegel:
Private estimation algorithms for stochastic block models and mixture models. CoRR abs/2301.04822 (2023) - [i62]Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni:
Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees. CoRR abs/2302.00037 (2023) - [i61]Vincent Cohen-Addad, Hung Le, Marcin Pilipczuk, Michal Pilipczuk:
Planar and Minor-Free Metrics Embed into Metrics of Polylogarithmic Treewidth with Expected Multiplicative Distortion Arbitrarily Close to 1. CoRR abs/2304.07268 (2023) - [i60]Lorenzo Beretta, Vincent Cohen-Addad, Silvio Lattanzi, Nikos Parotsidis:
Multi-Swap k-Means++. CoRR abs/2309.16384 (2023) - [i59]Vincent Cohen-Addad, Euiwoong Lee, Shi Li, Alantha Newman:
Handling Correlated Rounding Error via Preclustering: A 1.73-approximation for Correlation Clustering. CoRR abs/2309.17243 (2023) - [i58]Vincent Cohen-Addad, David P. Woodruff, Samson Zhou:
Streaming Euclidean k-median and k-means with o(log n) Space. CoRR abs/2310.02882 (2023) - [i57]Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation. CoRR abs/2310.04076 (2023) - [i56]Vincent Cohen-Addad, Chenglin Fan, Suprovat Ghoshal, Euiwoong Lee, Arnaud de Mesmay, Alantha Newman, Tony Chang Wang:
A PTAS for 𝓁0-Low Rank Approximation: Solving Dense CSPs over Reals. CoRR abs/2311.00892 (2023) - [i55]Ainesh Bakshi, Vincent Cohen-Addad, Samuel B. Hopkins, Rajesh Jayaram, Silvio Lattanzi:
A quasi-polynomial time algorithm for Multi-Dimensional Scaling via LP hierarchies. CoRR abs/2311.17840 (2023) - 2022
- [c65]Vincent Cohen-Addad, Yunus Esencayi, Chenglin Fan, Marco Gaboardi, Shi Li, Di Wang:
On Facility Location Problem in the Local Differential Privacy Model. AISTATS 2022: 3914-3929 - [c64]Vincent Cohen-Addad, Frederik Mallmann-Trenn, David Saulpic:
Community Recovery in the Degree-Heterogeneous Stochastic Block Model. COLT 2022: 1662-1692 - [c63]Vincent Cohen-Addad, Chenglin Fan, Euiwoong Lee, Arnaud de Mesmay:
Fitting Metrics and Ultrametrics with Minimum Disagreements. FOCS 2022: 301-311 - [c62]Vladimir Braverman, Vincent Cohen-Addad, Shaofeng H.-C. Jiang, Robert Krauthgamer, Chris Schwiegelshohn, Mads Bech Toftrup, Xuan Wu:
The Power of Uniform Sampling for Coresets. FOCS 2022: 462-473 - [c61]Vincent Cohen-Addad, Euiwoong Lee, Alantha Newman:
Correlation Clustering with Sherali-Adams. FOCS 2022: 651-661 - [c60]Amir Abboud, Vincent Cohen-Addad, Euiwoong Lee, Pasin Manurangsi:
Improved Approximation Algorithms and Lower Bounds for Search-Diversification Problems. ICALP 2022: 7:1-7:18 - [c59]Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis:
Online and Consistent Correlation Clustering. ICML 2022: 4157-4179 - [c58]Vincent Cohen-Addad, Vahab S. Mirrokni, Peilin Zhong:
Massively Parallel k-Means Clustering for Perturbation Resilient Instances. ICML 2022: 4180-4201 - [c57]Vincent Cohen-Addad, Tobias Mömke, Victor Verdugo:
A 2-Approximation for the Bounded Treewidth Sparsest Cut Problem in FPT Time. IPCO 2022: 112-125 - [c56]Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab Mirrokni, Andres Muñoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii:
Scalable Differentially Private Clustering via Hierarchically Separated Trees. KDD 2022: 221-230 - [c55]Vincent Cohen-Addad, Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong:
Near-Optimal Private and Scalable $k$-Clustering. NeurIPS 2022 - [c54]Vincent Cohen-Addad, Chenglin Fan, Silvio Lattanzi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski:
Near-Optimal Correlation Clustering with Privacy. NeurIPS 2022 - [c53]Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar:
Improved Coresets for Euclidean k-Means. NeurIPS 2022 - [c52]Vincent Cohen-Addad, Frederik Mallmann-Trenn, David Saulpic:
A Massively Parallel Modularity-Maximizing Algorithm with Provable Guarantees. PODC 2022: 356-365 - [c51]Vincent Cohen-Addad, Karthik C. S., Euiwoong Lee:
Johnson Coverage Hypothesis: Inapproximability of k-means and k-median in ℓp-metrics. SODA 2022: 1493-1530 - [c50]Vincent Cohen-Addad, Anupam Gupta, Lunjia Hu, Hoon Oh, David Saulpic:
An Improved Local Search Algorithm for k-Median. SODA 2022: 1556-1612 - [c49]Vincent Cohen-Addad:
Bypassing the surface embedding: approximation schemes for network design in minor-free graphs. STOC 2022: 343-356 - [c48]Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn:
Towards optimal lower bounds for k-median and k-means coresets. STOC 2022: 1038-1051 - [c47]Vincent Cohen-Addad, Hossein Esfandiari, Vahab S. Mirrokni, Shyam Narayanan:
Improved approximations for Euclidean k-means and k-median, via nested quasi-independent sets. STOC 2022: 1621-1628 - [i54]Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn:
Towards Optimal Lower Bounds for k-median and k-means Coresets. CoRR abs/2202.12793 (2022) - [i53]Vincent Cohen-Addad, Chenglin Fan, Silvio Lattanzi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski:
Near-Optimal Correlation Clustering with Privacy. CoRR abs/2203.01440 (2022) - [i52]Amir Abboud, Vincent Cohen-Addad, Euiwoong Lee, Pasin Manurangsi:
Improved Approximation Algorithms and Lower Bounds for Search-Diversification Problems. CoRR abs/2203.01857 (2022) - [i51]Vincent Cohen-Addad, Hossein Esfandiari, Vahab S. Mirrokni, Shyam Narayanan:
Improved Approximations for Euclidean k-means and k-median, via Nested Quasi-Independent Sets. CoRR abs/2204.04828 (2022) - [i50]Limor Gultchin, Vincent Cohen-Addad, Sophie Giffard-Roisin, Varun Kanade, Frederik Mallmann-Trenn:
Beyond Impossibility: Balancing Sufficiency, Separation and Accuracy. CoRR abs/2205.12327 (2022) - [i49]Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab S. Mirrokni, Andres Muñoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii:
Scalable Differentially Private Clustering via Hierarchically Separated Trees. CoRR abs/2206.08646 (2022) - [i48]Vincent Cohen-Addad, Fabrizio Grandoni, Euiwoong Lee, Chris Schwiegelshohn:
Breaching the 2 LMP Approximation Barrier for Facility Location with Applications to k-Median. CoRR abs/2207.05150 (2022) - [i47]Vincent Cohen-Addad, Euiwoong Lee, Alantha Newman:
Correlation Clustering with Sherali-Adams. CoRR abs/2207.10889 (2022) - [i46]Vincent Cohen-Addad, Chenglin Fan, Euiwoong Lee, Arnaud de Mesmay:
Fitting Metrics and Ultrametrics with Minimum Disagreements. CoRR abs/2208.13920 (2022) - [i45]Vincent Cohen-Addad, Jason Li:
On the Fixed-Parameter Tractability of Capacitated Clustering. CoRR abs/2208.14129 (2022) - [i44]Vladimir Braverman, Vincent Cohen-Addad, Shaofeng H.-C. Jiang, Robert Krauthgamer, Chris Schwiegelshohn, Mads Bech Toftrup, Xuan Wu:
The Power of Uniform Sampling for Coresets. CoRR abs/2209.01901 (2022) - [i43]Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar:
Improved Coresets for Euclidean k-Means. CoRR abs/2211.08184 (2022) - [i42]Vincent Cohen-Addad, Xi Chen, Rajesh Jayaram, Amit Levi, Erik Waingarten:
Streaming Euclidean MST to a Constant Factor. CoRR abs/2212.06546 (2022) - [i41]Siddhartha Banerjee, Vincent Cohen-Addad, Anupam Gupta, Zhouzi Li:
Graph Searching with Predictions. CoRR abs/2212.14220 (2022) - 2021
- [j8]Vincent Cohen-Addad, Éric Colin de Verdière, Dániel Marx, Arnaud de Mesmay:
Almost Tight Lower Bounds for Hard Cutting Problems in Embedded Graphs. J. ACM 68(4): 30:1-30:26 (2021) - [j7]Vincent Cohen-Addad, Andreas Emil Feldmann, David Saulpic:
Near-linear Time Approximation Schemes for Clustering in Doubling Metrics. J. ACM 68(6): 44:1-44:34 (2021) - [j6]Simon Mauras, Vincent Cohen-Addad, Guillaume Duboc, Max Dupré la Tour, Paolo Frasca, Claire Mathieu, Lulla Opatowski, Laurent Viennot:
Mitigating COVID-19 outbreaks in workplaces and schools by hybrid telecommuting. PLoS Comput. Biol. 17(8) (2021) - [j5]Vincent Cohen-Addad, Éric Colin de Verdière, Arnaud de Mesmay:
A Near-Linear Approximation Scheme for Multicuts of Embedded Graphs With a Fixed Number of Terminals. SIAM J. Comput. 50(1): 1-31 (2021) - [c46]Vincent Cohen-Addad, Benjamin Guedj, Varun Kanade, Guy Rom:
Online k-means Clustering. AISTATS 2021: 1126-1134 - [c45]Vincent Cohen-Addad, Debarati Das, Evangelos Kipouridis, Nikos Parotsidis, Mikkel Thorup:
Fitting Distances by Tree Metrics Minimizing the Total Error within a Constant Factor. FOCS 2021: 468-479 - [c44]Vincent Cohen-Addad, Philip N. Klein, Dániel Marx, Archer Wheeler, Christopher Wolfram:
On the Computational Tractability of a Geographic Clustering Problem Arising in Redistricting. FORC 2021: 3:1-3:18 - [c43]Vincent Cohen-Addad, Rémi de Joannis de Verclos, Guillaume Lagarde:
Improving Ultrametrics Embeddings Through Coresets. ICML 2021: 2060-2068 - [c42]Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski:
Correlation Clustering in Constant Many Parallel Rounds. ICML 2021: 2069-2078 - [c41]Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, Ola Svensson:
Parallel and Efficient Hierarchical k-Median Clustering. NeurIPS 2021: 20333-20345 - [c40]Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces. NeurIPS 2021: 21085-21098 - [c39]Vincent Cohen-Addad, Karthik C. S., Euiwoong Lee:
On Approximability of Clustering Problems Without Candidate Centers. SODA 2021: 2635-2648 - [c38]Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
A new coreset framework for clustering. STOC 2021: 169-182 - [c37]Vincent Cohen-Addad, Anupam Gupta, Philip N. Klein, Jason Li:
A quasipolynomial (2 + ε)-approximation for planar sparsest cut. STOC 2021: 1056-1069 - [i40]Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
A New Coreset Framework for Clustering. CoRR abs/2104.06133 (2021) - [i39]Vincent Cohen-Addad, Anupam Gupta, Philip N. Klein, Jason Li:
A Quasipolynomial (2+ε)-Approximation for Planar Sparsest Cut. CoRR abs/2105.15187 (2021) - [i38]Karl Bringmann, Vincent Cohen-Addad, Debarati Das:
A Linear-Time n0.4-Approximation for Longest Common Subsequence. CoRR abs/2106.08195 (2021) - [i37]Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski:
Correlation Clustering in Constant Many Parallel Rounds. CoRR abs/2106.08448 (2021) - [i36]Vincent Cohen-Addad, Debarati Das, Evangelos Kipouridis, Nikos Parotsidis, Mikkel Thorup:
Fitting Distances by Tree Metrics Minimizing the Total Error within a Constant Factor. CoRR abs/2110.02807 (2021) - [i35]Vincent Cohen-Addad, Anupam Gupta, Lunjia Hu, Hoon Oh, David Saulpic:
An Improved Local Search Algorithm for k-Median. CoRR abs/2111.04589 (2021) - [i34]Vincent Cohen-Addad, Tobias Mömke, Victor Verdugo:
A 2-Approximation for the Bounded Treewidth Sparsest Cut Problem in FPT Time. CoRR abs/2111.06163 (2021) - [i33]Vincent Cohen-Addad, Karthik C. S., Euiwoong Lee:
Johnson Coverage Hypothesis: Inapproximability of k-means and k-median in L_p metrics. CoRR abs/2111.10912 (2021) - [i32]Amir Abboud, MohammadHossein Bateni, Vincent Cohen-Addad, Karthik C. S., Saeed Seddighin:
On Complexity of 1-Center in Various Metrics. CoRR abs/2112.03222 (2021) - [i31]Vincent Cohen-Addad, Karthik C. S., Euiwoong Lee:
Johnson Coverage Hypothesis: Inapproximability of k-means and k-median in $\ell_p$-metrics. Electron. Colloquium Comput. Complex. TR21 (2021) - 2020
- [c36]Vincent Cohen-Addad, Arnold Filtser, Philip N. Klein, Hung Le:
On Light Spanners, Low-treewidth Embeddings and Efficient Traversing in Minor-free Graphs. FOCS 2020: 589-600 - [c35]Vincent Cohen-Addad, Karthik C. S., Guillaume Lagarde:
On Efficient Low Distortion Ultrametric Embedding. ICML 2020: 2078-2088 - [c34]Vincent Cohen-Addad, Adrian Kosowski, Frederik Mallmann-Trenn, David Saulpic:
On the Power of Louvain in the Stochastic Block Model. NeurIPS 2020 - [c33]Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, Ola Svensson:
Fast and Accurate $k$-means++ via Rejection Sampling. NeurIPS 2020 - [c32]Vincent Cohen-Addad, Frederik Mallmann-Trenn, Claire Mathieu:
Instance-Optimality in the Noisy Value-and Comparison-Model. SODA 2020: 2124-2143 - [c31]Vincent Cohen-Addad:
Approximation Schemes for Capacitated Clustering in Doubling Metrics. SODA 2020: 2241-2259 - [c30]Amir Abboud, Vincent Cohen-Addad, Philip N. Klein:
New hardness results for planar graph problems in p and an algorithm for sparsest cut. STOC 2020: 996-1009 - [i30]Amir Abboud, Vincent Cohen-Addad, Philip N. Klein:
New Hardness Results for Planar Graph Problems in P and an Algorithm for Sparsest Cut. CoRR abs/2007.02377 (2020) - [i29]Vincent Cohen-Addad, Karthik C. S., Guillaume Lagarde:
On Efficient Low Distortion Ultrametric Embedding. CoRR abs/2008.06700 (2020) - [i28]Vincent Cohen-Addad, Philip N. Klein, Dániel Marx:
On the computational tractability of a geographic clustering problem arising in redistricting. CoRR abs/2009.00188 (2020) - [i27]Vincent Cohen-Addad, Arnold Filtser, Philip N. Klein, Hung Le:
On Light Spanners, Low-treewidth Embeddings and Efficient Traversing in Minor-free Graphs. CoRR abs/2009.05039 (2020) - [i26]Vincent Cohen-Addad, Karthik C. S., Euiwoong Lee:
On Approximability of Clustering Problems Without Candidate Centers. CoRR abs/2010.00087 (2020) - [i25]Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, Ola Svensson:
Fast and Accurate k-means++ via Rejection Sampling. CoRR abs/2012.11891 (2020)
2010 – 2019
- 2019
- [j4]Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn, Claire Mathieu:
Hierarchical Clustering: Objective Functions and Algorithms. J. ACM 66(4): 26:1-26:42 (2019) - [j3]Vincent Cohen-Addad, Philip N. Klein, Claire Mathieu:
Local Search Yields Approximation Schemes for k-Means and k-Median in Euclidean and Minor-Free Metrics. SIAM J. Comput. 48(2): 644-667 (2019) - [c29]Vincent Cohen-Addad, Éric Colin de Verdière, Dániel Marx, Arnaud de Mesmay:
Almost Tight Lower Bounds for Hard Cutting Problems in Embedded Graphs. SoCG 2019: 27:1-27:16 - [c28]Vincent Cohen-Addad, Marcin Pilipczuk, Michal Pilipczuk:
Efficient Approximation Schemes for Uniform-Cost Clustering Problems in Planar Graphs. ESA 2019: 33:1-33:14 - [c27]Vincent Cohen-Addad, Karthik C. S.:
Inapproximability of Clustering in Lp Metrics. FOCS 2019: 519-539 - [c26]