Seyed Esmaeili — accepted AI-conference papers

2023

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

Seyed Esmaeili, Sharmila Duppala, Davidson Cheng, Vedant Nanda, Aravind Srinivasan, John P. Dickerson

AAAI 2023technical

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 of three entities: two sides to be matched and a platform operator that decides the matching. The design of algorithms fo…

Cited by 27 Source ↗PDF ↗Scholar ↗

2020

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

Chris Decarolis, Mukul Ram, Seyed Esmaeili, Yu-Xiang Wang, Furong Huang

ICML 2020poster

We provide an end-to-end differentially private spectral algorithm for learning LDA, based on matrix/tensor decompositions, and establish theoretical guarantees on utility/consistency of the estimated model parameters. We represent the spectral algorithm as a computational graph. Noise can be inject…

Cited by 12 Source ↗PDF ↗Scholar ↗

2020

Probabilistic Fair Clustering

Seyed Esmaeili, Brian Brubach, Leonidas Tsepenekas, John Dickerson

NeurIPS 2020poster

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 fair clustering problems, vertices are endowed with a color (e.g., membership in a group), and the requirements of a v…

Cited by 51 Source ↗PDF ↗Scholar ↗