10 citations · 21 across the 15 of their papers we have counts for
20 papers · 1 filter
A Better-than-2 Approximation for k-Median Part I: The Power of General Bids
Rares-Darius Buhai, Vincent Cohen-Addad, Henry Fleischmann +6
The Greedy algorithm of Jain, Mahdian, Markakis, Saberi, and Vazirani was the best Lagrangian-multiplier-preserving (LMP) approximation algorithm for facility location, and thus th…
A Better-than- Approximation for -Median Part II: Stable Instances
Rares-Darius Buhai, Vincent Cohen-Addad, Henry Fleischmann +6
We consider the -median problem on general metric spaces, subject to the condition that the instances are -stable (i.e., the optimal solution using centers has a cost a…
Spectral Dual Fitting for -Means
Aditya Anand, Moses Charikar, Vincent Cohen-Addad +5
We give a new dual fitting algorithm which gives improved approximation ratios of and for -Means in (high-dimensional) Euclidean and genera…
An Improved Greedy Approximation for (Metric) -Means
Moses Charikar, Vincent Cohen-Addad, Ruiquan Gao +3
Clustering is a basic task in data analysis and machine learning, and the optimization of clustering objectives are well-studied optimization problems; amongst these, the -Means…
Complexity of Local Search for CSPs Parameterized by Constraint Difference
Aditya Anand, Vincent Cohen-Addad, Tommaso d'Orsi +4
In this paper, we study the parameterized complexity of local search, whose goal is to find a good nearby solution from the given current solution. Formally, given an optimization…
Combinatorial Optimization using Comparison Oracles
Vincent Cohen-Addad, Tommaso d'Orsi, Anupam Gupta +7
In linear combinatorial optimization, we aim to find for a family over a ground set…
Static to Dynamic Correlation Clustering
Nairen Cao, Vincent Cohen-Addad, Euiwoong Lee +7
Correlation clustering is a well-studied problem, first proposed by Bansal, Blum, and Chawla [Mach. Learn. '04]. The input is an unweighted, undirected graph. The problem is to clu…
Solving the Correlation Cluster LP in Sublinear Time
Nairen Cao, Vincent Cohen-Addad, Shi Li +7
Correlation Clustering is a fundamental and widely-studied problem in unsupervised learning and data mining. The input is a graph and the goal is to construct a clustering minimizi…
A -Approximation Algorithm for Metric -Median
Vincent Cohen-Addad, Fabrizio Grandoni, Euiwoong Lee +2
In the classical NP-hard metric -median problem, we are given a set of clients and centers with metric distances between them, along with an integer parameter . The…
Understanding the Cluster LP for Correlation Clustering
Nairen Cao, Vincent Cohen-Addad, Euiwoong Lee +3
In the classic Correlation Clustering problem introduced by Bansal, Blum, and Chawla (FOCS 2002), the input is a complete graph where edges are labeled either or , and the g…
Correlation Clustering Beyond the Pivot Algorithm
Soheil Behnezhad, Moses Charikar, Vincent Cohen-Addad +2
We study the classic correlation clustering in the dynamic setting. Given objects and a complete labeling of the object-pairs as either similar or dissimilar, the goal is to pa…
Max-Cut with -Accurate Predictions
Vincent Cohen-Addad, Tommaso d'Orsi, Anupam Gupta +2
We study the approximability of the MaxCut problem in the presence of predictions. Specifically, we consider two models: in the noisy predictions model, for each vertex we are give…
A PTAS for -Low Rank Approximation: Solving Dense CSPs over Reals
Vincent Cohen-Addad, Chenglin Fan, Suprovat Ghoshal +4
We consider the Low Rank Approximation problem, where the input consists of a matrix and an integer , and the goal is to find a matrix of…
Handling Correlated Rounding Error via Preclustering: A 1.73-approximation for Correlation Clustering
Vincent Cohen-Addad, Euiwoong Lee, Shi Li +1
We consider the classic Correlation Clustering problem: Given a complete graph where edges are labelled either or , the goal is to find a partition of the vertices that mini…
Fitting Metrics and Ultrametrics with Minimum Disagreements
Vincent Cohen-Addad, Chenglin Fan, Euiwoong Lee +1
Given recording pairwise distances, the METRIC VIOLATION DISTANCE (MVD) problem asks to compute the distance between and…
Correlation Clustering with Sherali-Adams
Vincent Cohen-Addad, Euiwoong Lee, Alantha Newman
Given a complete graph where each edge is labeled or , the Correlation Clustering problem asks to partition into clusters to minimize the number of edges…
Breaching the 2 LMP Approximation Barrier for Facility Location with Applications to k-Median
Vincent Cohen-Addad, Fabrizio Grandoni, Euiwoong Lee +1
The Uncapacitated Facility Location (UFL) problem is one of the most fundamental clustering problems: Given a set of clients and a set of facilities in a metric space $(C \…
Improved Approximation Algorithms and Lower Bounds for Search-Diversification Problems
Amir Abboud, Vincent Cohen-Addad, Euiwoong Lee +1
We study several questions related to diversifying search results. We give improved approximation algorithms in each of the following problems, together with some lower bounds. - W…
Fitting Distances by Tree Metrics Minimizing the Total Error within a Constant Factor
Vincent Cohen-Addad, Debarati Das, Evangelos Kipouridis +2
We consider the numerical taxonomy problem of fitting a positive distance function by a tree metric. We want a tree with positive e…
Tight FPT Approximations for -Median and -Means
Vincent Cohen-Addad, Anupam Gupta, Amit Kumar +2
We investigate the fine-grained complexity of approximating the classical -median / -means clustering problems in general metric spaces. We show how to improve the approximat…