7 citations · 10 across the 8 of their papers we have counts for
6 papers · 1 filter
Learning-Augmented Hierarchical Clustering
Vladimir Braverman, Jon C. Ergun, Chen Wang +1
Hierarchical clustering (HC) is an important data analysis technique in which the goal is to recursively partition a dataset into a tree-like structure while grouping together simi…
Relative Error Fair Clustering in the Weak-Strong Oracle Model
Vladimir Braverman, Prathamesh Dharangutte, Shaofeng H. -C. Jiang +4
We study fair clustering problems in a setting where distance information is obtained from two sources: a strong oracle providing exact distances, but at a high cost, and a weak or…
Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time
Vladimir Braverman, Prathamesh Dharangutte, Shreyas Pai +2
We study the dynamic correlation clustering problem with edge label flips. In correlation clustering, we are given a -vertex complete graph whose edges are l…
Learning-augmented Maximum Independent Set
Vladimir Braverman, Prathamesh Dharangutte, Vihan Shah +1
We study the Maximum Independent Set (MIS) problem on general graphs within the framework of learning-augmented algorithms. The MIS problem is known to be NP-hard and is also NP-ha…
Parallel Approximate Maximum Flows in Near-Linear Work and Polylogarithmic Depth
Arpit Agarwal, Sanjeev Khanna, Huan Li +4
We present a parallel algorithm for the -approximate maximum flow problem in capacitated, undirected graphs with vertices and edges, achieving $O(ε^{-3}\text{polylog…
Metric Clustering and MST with Strong and Weak Distance Oracles
MohammadHossein Bateni, Prathamesh Dharangutte, Rajesh Jayaram +1
We study optimization problems in a metric space where we can compute distances in two ways: via a ''strong'' oracle that returns exact distances , and a…