Search Results for author: Seyed A. Esmaeili

Found 9 papers, 1 papers with code

An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm

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.

Variational Inference

Fast-AT: Fast Automatic Thumbnail Generation using Deep Neural Networks

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.

Probabilistic Fair Clustering

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.

Clustering valid

A New Notion of Individually Fair Clustering: $α$-Equitable $k$-Center

1 code implementation9 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.

Clustering Fairness

Fair Clustering Under a Bounded Cost

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.

Clustering Fairness

Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual

no code implementations16 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.

Fairness Vocal Bursts Valence Prediction

Implications of Distance over Redistricting Maps: Central and Outlier Maps

no code implementations2 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.

Fairness Outlier Detection +1

Fair Labeled Clustering

no code implementations28 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.

Clustering Fairness

Robust and Performance Incentivizing Algorithms for Multi-Armed Bandits with Strategic Agents

no code implementations13 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.

Multi-Armed Bandits

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