no code implementations • 16 Aug 2023 • Ragesh Jaiswal
We give a quantum approximation scheme (i. e., $(1 + \varepsilon)$-approximation for every $\varepsilon > 0$) for the classical $k$-means clustering problem in the QRAM model with a running time that has only polylogarithmic dependence on the number of data points.
no code implementations • 26 May 2023 • Ragesh Jaiswal, Amit Kumar
Coresets for $k$-means and $k$-median problems yield a small summary of the data, which preserve the clustering cost with respect to any set of $k$ centers.
no code implementations • 27 Oct 2021 • Dishant Goyal, Ragesh Jaiswal
In this work, we study a range of constrained versions of the $k$-supplier and $k$-center problems such as: capacitated, fault-tolerant, fair, etc.
no code implementations • 12 Jun 2021 • Dishant Goyal, Ragesh Jaiswal
The goal in the socially fair $k$-median problem is to find a set $C \subseteq F$ of $k$ centers that minimizes the maximum average cost over all the groups.
no code implementations • 9 Nov 2020 • Anup Bhattacharya, Dishant Goyal, Ragesh Jaiswal
In this setting, we show the first hardness of approximation result for the Euclidean $k$-median problem for any $\beta < 1. 015$, assuming UGC.
no code implementations • NeurIPS 2009 • Nir Ailon, Ragesh Jaiswal, Claire Monteleoni
We provide a clustering algorithm that approximately optimizes the k-means objective, in the one-pass streaming setting.