5 citations · 13 across the 8 of their papers we have counts for
8 papers
Doubly Constrained Fair Clustering
John Dickerson, Seyed A. Esmaeili, Jamie Morgenstern +1
The remarkable attention which fair clustering has received in the last few years has resulted in a significant number of different notions of fairness. Despite the fact that these…
Fair Labeled Clustering
Seyed A. Esmaeili, Sharmila Duppala, John P. Dickerson +1
Numerous algorithms have been produced for the fundamental problem of clustering under many different notions of fairness. Perhaps the most common family of notions currently studi…
Implications of Distance over Redistricting Maps: Central and Outlier Maps
Seyed A. Esmaeili, Darshan Chakrabarti, Hayley Grape +1
In representative democracy, a redistricting map is chosen to partition an electorate into districts which each elects a representative. A valid redistricting map must satisfy a co…
Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual
Seyed A. Esmaeili, Sharmila Duppala, Davidson Cheng +3
Online bipartite-matching platforms are ubiquitous and find applications in important areas such as crowdsourcing and ridesharing. In the most general form, the platform consists o…
Fair Clustering Under a Bounded Cost
Seyed A. Esmaeili, Brian Brubach, Aravind Srinivasan +1
Clustering is a fundamental unsupervised learning problem where a dataset is partitioned into clusters that consist of nearby points in a metric space. A recent variant, fair clust…
A New Notion of Individually Fair Clustering: -Equitable -Center
Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili +2
Clustering is a fundamental problem in unsupervised machine learning, and fair variants of it have recently received significant attention due to its societal implications. In this…
Probabilistic Fair Clustering
Seyed A. Esmaeili, Brian Brubach, Leonidas Tsepenekas +1
In clustering problems, a central decision-maker is given a complete metric graph over vertices and must provide a clustering of vertices that minimizes some objective function. In…
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm
Christopher DeCarolis, Mukul Ram, Seyed A. Esmaeili +2
We provide an end-to-end differentially private spectral algorithm for learning LDA, based on matrix/tensor decompositions, and establish theoretical guarantees on utility/consiste…