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ORCIDVincent Cohen-Addad
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- affiliation: Google Zurich, Switzerland
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2020 – today
- 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)- [c71]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 - [c70]Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni:
Differentially Private Hierarchical Clustering with Provable Approximation Guarantees. ICML 2023: 14353-14375 - [c69]Siddhartha Banerjee, Vincent Cohen-Addad, Anupam Gupta, Zhouzi Li:
Graph Searching with Predictions. ITCS 2023: 12:1-12:24 - [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 - [i57]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) - [i56]Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni:
Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees. CoRR abs/2302.00037 (2023) - [i55]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) - 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]David Saulpic, Vincent Cohen-Addad, Andreas Emil Feldmann:
Near-Linear Time Approximations Schemes for Clustering in Doubling Metrics. FOCS 2019: 540-559 - [c25]Vincent Cohen-Addad, Michal Pilipczuk, Marcin Pilipczuk:
A Polynomial-Time Approximation Scheme for Facility Location on Planar Graphs. FOCS 2019: 560-581 - [c24]Vincent Cohen-Addad, Jason Li:
On the Fixed-Parameter Tractability of Capacitated Clustering. ICALP 2019: 41:1-41:14 - [c23]Vincent Cohen-Addad, Anupam Gupta, Amit Kumar, Euiwoong Lee, Jason Li:
Tight FPT Approximations for k-Median and k-Means. ICALP 2019: 42:1-42:14 - [c22]Vincent Cohen-Addad, Niklas Hjuler, Nikos Parotsidis, David Saulpic, Chris Schwiegelshohn:
Fully Dynamic Consistent Facility Location. NeurIPS 2019: 3250-3260 - [c21]Amir Abboud, Vincent Cohen-Addad, Hussein Houdrouge:
Subquadratic High-Dimensional Hierarchical Clustering. NeurIPS 2019: 11576-11586 - [c20]Vincent Cohen-Addad, Laurent Feuilloley, Tatiana Starikovskaya:
Lower bounds for text indexing with mismatches and differences. SODA 2019: 1146-1164 - [c19]Luca Becchetti, Marc Bury, Vincent Cohen-Addad, Fabrizio Grandoni, Chris Schwiegelshohn:
Oblivious dimension reduction for k-means: beyond subspaces and the Johnson-Lindenstrauss lemma. STOC 2019: 1039-1050 - [i24]Vincent Cohen-Addad, Éric Colin de Verdière, Dániel Marx, Arnaud de Mesmay:
Almost Tight Lower Bounds for Hard Cutting Problems in Embedded Graphs. CoRR abs/1903.08603 (2019) - [i23]Vincent Cohen-Addad, Marcin Pilipczuk, Michal Pilipczuk:
A Polynomial-Time Approximation Scheme for Facility Location on Planar Graphs. CoRR abs/1904.10680 (2019) - [i22]Vincent Cohen-Addad, Anupam Gupta, Amit Kumar, Euiwoong Lee, Jason Li:
Tight FPT Approximations for $k$-Median and k-Means. CoRR abs/1904.12334 (2019) - [i21]Vincent Cohen-Addad, Marcin Pilipczuk, Michal Pilipczuk:
Efficient approximation schemes for uniform-cost clustering problems in planar graphs. CoRR abs/1905.00656 (2019) - [i20]Vincent Cohen-Addad, Benjamin Guedj, Varun Kanade, Guy Rom:
Online k-means Clustering. CoRR abs/1909.06861 (2019) - 2018
- [c18]Vincent Cohen-Addad, Philip N. Klein, Neal E. Young:
Balanced centroidal power diagrams for redistricting. SIGSPATIAL/GIS 2018: 389-396 - [c17]Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn:
Clustering Redemption-Beyond the Impossibility of Kleinberg's Axioms. NeurIPS 2018: 8526-8535 - [c16]Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn, Claire Mathieu:
Hierarchical Clustering: Objective Functions and Algorithms. SODA 2018: 378-397 - [c15]Vincent Cohen-Addad:
A Fast Approximation Scheme for Low-Dimensional k-Means. SODA 2018: 430-440 - [c14]Vincent Cohen-Addad, Arnaud de Mesmay, Eva Rotenberg, Alan Roytman:
The Bane of Low-Dimensionality Clustering. SODA 2018: 441-456 - [c13]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. SODA 2018: 1439-1458 - [c12]Mikkel Abrahamsen, Anna Adamaszek, Karl Bringmann, Vincent Cohen-Addad, Mehran Mehr, Eva Rotenberg, Alan Roytman, Mikkel Thorup:
Fast fencing. STOC 2018: 564-573 - [i19]Mikkel Abrahamsen, Anna Adamaszek, Karl Bringmann, Vincent Cohen-Addad, Mehran Mehr, Eva Rotenberg, Alan Roytman, Mikkel Thorup:
Fast Fencing. CoRR abs/1804.00101 (2018) - [i18]Vincent Cohen-Addad, Frederik Mallmann-Trenn, Claire Mathieu:
Instance-Optimality in the Noisy Value-and Comparison-Model - Accept, Accept, Strong Accept: Which Papers get in? CoRR abs/1806.08182 (2018) - [i17]Vincent Cohen-Addad:
Approximation Schemes for Capacitated Clustering in Doubling Metrics. CoRR abs/1812.07721 (2018) - [i16]Vincent Cohen-Addad, Andreas Emil Feldmann, David Saulpic:
Near-Linear Time Approximation Schemes for Clustering in Doubling Metrics. CoRR abs/1812.08664 (2018) - [i15]Vincent Cohen-Addad, Laurent Feuilloley, Tatiana Starikovskaya:
Lower bounds for text indexing with mismatches and differences. CoRR abs/1812.09120 (2018) - 2017
- [j2]Vincent Cohen-Addad, Michael Hebdige, Daniel Král', Zhentao Li, Esteban Salgado:
Steinberg's Conjecture is false. J. Comb. Theory, Ser. B 122: 452-456 (2017) - [c11]Vincent Cohen-Addad, Varun Kanade:
Online Optimization of Smoothed Piecewise Constant Functions. AISTATS 2017: 412-420 - [c10]Vincent Cohen-Addad, Chris Schwiegelshohn:
On the Local Structure of Stable Clustering Instances. FOCS 2017: 49-60 - [c9]Vincent Cohen-Addad, Søren Dahlgaard, Christian Wulff-Nilsen:
Fast and Compact Exact Distance Oracle for Planar Graphs. FOCS 2017: 962-973 - [c8]Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn:
Hierarchical Clustering Beyond the Worst-Case. NIPS 2017: 6201-6209 - [i14]Vincent Cohen-Addad, Chris Schwiegelshohn:
One Size Fits All : Effectiveness of Local Search on Structured Data. CoRR abs/1701.08423 (2017) - [i13]Vincent Cohen-Addad, Søren Dahlgaard, Christian Wulff-Nilsen:
Fast and Compact Exact Distance Oracle for Planar Graphs. CoRR abs/1702.03259 (2017) - [i12]Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn, Claire Mathieu:
Hierarchical Clustering: Objective Functions and Algorithms. CoRR abs/1704.02147 (2017) - [i11]Vincent Cohen-Addad:
A Fast Approximation Scheme for Low-Dimensional k-Means. CoRR abs/1708.07381 (2017) - [i10]Vincent Cohen-Addad, Philip N. Klein, Neal E. Young:
Balanced power diagrams for redistricting. CoRR abs/1710.03358 (2017) - [i9]Vincent Cohen-Addad, Arnaud de Mesmay, Eva Rotenberg, Alan Roytman:
The Bane of Low-Dimensionality Clustering. CoRR abs/1711.01171 (2017) - 2016
- [j1]Vincent Cohen-Addad, Michel Habib, Fabien de Montgolfier:
Algorithmic aspects of switch cographs. Discret. Appl. Math. 200: 23-42 (2016) - [c7]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. FOCS 2016: 353-364 - [c6]Vincent Cohen-Addad, Chris Schwiegelshohn, Christian Sohler:
Diameter and k-Center in Sliding Windows. ICALP 2016: 19:1-19:12 - [c5]Vincent Cohen-Addad, Alon Eden, Michal Feldman, Amos Fiat:
The Invisible Hand of Dynamic Market Pricing. EC 2016: 383-400 - [c4]Vincent Cohen-Addad, Éric Colin de Verdière, Philip N. Klein, Claire Mathieu,