no code implementations • 13 Dec 2023 • Seyed A. Esmaeili, Suho Shin, Aleksandrs Slivkins
We identify a class of MAB algorithms which we call performance incentivizing which satisfy a collection of properties and show that they lead to mechanisms that incentivize top level performance at equilibrium and are robust under any strategy profile.
no code implementations • 28 May 2022 • Seyed A. Esmaeili, Sharmila Duppala, John P. Dickerson, Brian Brubach
To ensure group fairness in such a setting, we would desire proportional group representation in every label but not necessarily in every cluster as is done in group fair clustering.
no code implementations • 2 Mar 2022 • Seyed A. Esmaeili, Darshan Chakrabarti, Hayley Grape, Brian Brubach
Specifically, we define a central map which may be considered as being "most typical" and give a rigorous justification for it by showing that it mirrors the Kemeny ranking in a scenario where we have a committee voting over a collection of redistricting maps to be drawn.
no code implementations • 16 Jan 2022 • Seyed A. Esmaeili, Sharmila Duppala, Davidson Cheng, Vedant Nanda, Aravind Srinivasan, John P. Dickerson
Since fairness has become an important consideration that was ignored in the existing algorithms a collection of online matching algorithms have been developed that give a fair treatment guarantee for one side of the market at the expense of a drop in the operator's profit.
no code implementations • NeurIPS 2021 • Seyed A. Esmaeili, Brian Brubach, Aravind Srinivasan, John P. Dickerson
We derive fundamental lower bounds on the approximation of the utilitarian and egalitarian objectives and introduce algorithms with provable guarantees for them.
1 code implementation • 9 Jun 2021 • Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili, Aravind Srinivasan, Leonidas Tsepenekas
Clustering is a fundamental problem in unsupervised machine learning, and fair variants of it have recently received significant attention due to its societal implications.
no code implementations • NeurIPS 2020 • Seyed A. Esmaeili, Brian Brubach, Leonidas Tsepenekas, John P. Dickerson
In fair clustering problems, vertices are endowed with a color (e. g., membership in a group), and the features of a valid clustering might also include the representation of colors in that clustering.
no code implementations • ICML 2020 • Christopher DeCarolis, Mukul Ram, Seyed A. Esmaeili, Yu-Xiang Wang, Furong Huang
Overall, by combining the sensitivity and utility characterization, we obtain an end-to-end differentially private spectral algorithm for LDA and identify the corresponding configuration that outperforms others in any specific regime.
no code implementations • CVPR 2017 • Seyed A. Esmaeili, Bharat Singh, Larry S. Davis
It is a fully-convolutional deep neural network, which learns specific filters for thumbnails of different sizes and aspect ratios.