Search Results for author: Seyed A. Esmaeili

Found 7 papers, 1 papers with code

Centralized Fairness for Redistricting

no code implementations2 Mar 2022 Seyed A. Esmaeili, Hayley Grape, Brian Brubach

On the positive side, researchers have recently developed tools for measuring how gerrymandered a redistricting map is by comparing it to a large set of randomly-generated district maps.

Fairness

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

no code implementations16 Jan 2022 Seyed A. Esmaeili, Sharmila Duppala, Vedant Nanda, Aravind Srinivasan, John P. Dickerson

In the most general form, the platform consists of three entities: two sides to be matched and a platform operator that decides the matching.

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.

Fairness

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.

Fairness

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.

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.

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