1 citations · 2 across the 11 of their papers we have counts for
17 papers
On the Parameterized Approximability of (Mergeable) Sum of Radii Clustering
Ameet Gadekar
The sum of radii problem (-MSR) asks, given a metric space on points, to place balls covering all points so as to minimize the sum of their radii. Despite extensive stud…
FPT Approximations for Fair Sum of Radii with Outliers and General Norm Objectives
Ameet Gadekar
The sum of radii problem is a classical clustering problem in which, given a set of points and an integer , the goal is to place balls that cover while minimizing th…
Fair Committee Selection under Ordinal Preferences and Limited Cardinal Information
Ameet Gadekar, Aristides Gionis, Suhas Thejaswi +1
We study the problem of fair -committee selection under an egalitarian objective. Given agents partitioned into groups (\eg, demographic quotas), the goal is to aggregat…
Label-consistent clustering for evolving data
Ameet Gadekar, Aristides Gionis, Thibault Marette
Data analysis often involves an iterative process, where solutions must be continuously refined in response to new data. Typically, as new data becomes available, an existing solut…
Capacitated Fair-Range Clustering: Hardness and Approximation Algorithms
Ameet Gadekar, Suhas Thejaswi
Capacitated fair-range -clustering generalizes classical -clustering by incorporating both capacity constraints and demographic fairness. In this setting, each facility has a…
Clustering under Constraints: Efficient Parameterized Approximation Schemes
Sujoy Bhore, Ameet Gadekar, Tanmay Inamdar
We present a unified framework that yields EPASes for constrained -clustering in metric spaces of bounded (algorithmic) scatter dimension, a notion introduced by Abbasi et a…
Dimension-Free Parameterized Approximation Schemes for Hybrid Clustering
Ameet Gadekar, Tanmay Inamdar
Hybrid -Clustering is a model of clustering that generalizes two of the most widely studied clustering objectives: -Center and -Median. In this model, given a set of p…
Fair Clustering for Data Summarization: Improved Approximation Algorithms and Complexity Insights
Ameet Gadekar, Aristides Gionis, Suhas Thejaswi
Data summarization tasks are often modeled as -clustering problems, where the goal is to choose data points, called cluster centers, that best represent the dataset by minim…
FPT approximations for Capacitated Sum of Radii and Diameters
Arnold Filtser, Ameet Gadekar
The Capacitated Sum of Radii problem involves partitioning a set of points , where each point has capacity , into clusters that minimize the sum of cluster rad…
Diversity-aware clustering: Computational Complexity and Approximation Algorithms
Suhas Thejaswi, Ameet Gadekar, Bruno Ordozgoiti +1
In this work, we study diversity-aware clustering problems where the data points are associated with multiple attributes resulting in intersecting groups. A clustering solution nee…
Independent set in -Claw-Free Graphs: Conditional -boundedness and the Power of LP/SDP Relaxations
Parinya Chalermsook, Ameet Gadekar, Kamyar Khodamoradi +1
This paper studies -claw-free graphs, exploring the connection between an extremal combinatorics question and the power of a convex program in approximating the maximum-weight i…
Parameterized Approximation for Robust Clustering in Discrete Geometric Spaces
Fateme Abbasi, Sandip Banerjee, Jarosław Byrka +6
We consider the well-studied Robust -Clustering problem, which generalizes the classic -Median, -Means, and -Center problems. Given a constant , the input…
Parameterized Approximation Schemes for Clustering with General Norm Objectives
Fateme Abbasi, Sandip Banerjee, Jarosław Byrka +6
This paper considers the well-studied algorithmic regime of designing a -approximation algorithm for a -clustering problem that runs in time (sometimes ca…
Clustering with fair-center representation: parameterized approximation algorithms and heuristics
Suhas Thejaswi, Ameet Gadekar, Bruno Ordozgoiti +1
We study a variant of classical clustering formulations in the context of algorithmic fairness, known as diversity-aware clustering. In this variant we are given a collection of fa…
On the parameterized complexity of Compact Set Packing
Ameet Gadekar
The Set Packing problem is, given a collection of sets over a ground set , to find a maximum collection of sets that are pairwise disjoint. The problem i…
On the hardness of learning sparse parities
Arnab Bhattacharyya, Ameet Gadekar, Suprovat Ghoshal +1
This work investigates the hardness of computing sparse solutions to systems of linear equations over F_2. Consider the k-EvenSet problem: given a homogeneous system of linear equa…
On learning k-parities with and without noise
Arnab Bhattacharyya, Ameet Gadekar, Ninad Rajgopal
We first consider the problem of learning -parities in the on-line mistake-bound model: given a hidden vector with and a sequence of "questions" $a_1,…