1 citations · 2 across the 11 of their papers we have counts for
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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…