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Nisarg Shah

Department of Computer Science

18 papers hereh-index 313.9k citations85 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4
  • last author14

Across the 18 of 18 papers where every author was matched, so the position is known.

fields
  • cs.GT16
  • cs.AI1
  • cs.LO1
affiliations
  • Department of Computer Science
Homepage
same name
  • Nisarg Shah — 8 papers, h 4
  • Nisarg Shah — 6 papers, h 8
  • Nisarg Shah — 5 papers, h 3
  • Nisarg Shah — 2 papers
  • Nisarg Shah — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20112024
most citedA Maximum Likelihood Approach For Selecting Sets of Alternatives

35 citations · 79 across the 12 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.GT2022★ 1 cited

Proportionally Fair Online Allocation of Public Goods with Predictions

Siddhartha Banerjee, Vasilis Gkatzelis, Safwan Hossain +3

We design online algorithms for the fair allocation of public goods to a set of N agents over a sequence of T rounds and focus on improving their performance using predictions.…

cs.GT2022★ 10 cited

Optimized Distortion and Proportional Fairness in Voting

Soroush Ebadian, Anson Kahng, Dominik Peters +1

A voting rule decides on a probability distribution over a set of m alternatives, based on rankings of those alternatives provided by agents. We assume that agents have cardinal ut…

cs.GT2022

Class Fairness in Online Matching

Hadi Hosseini, Zhiyi Huang, Ayumi Igarashi +1

In the classical version of online bipartite matching, there is a given set of offline vertices (aka agents) and another set of vertices (aka items) that arrive online. When each i…

cs.GT2022

The Metric Distortion of Multiwinner Voting

Ioannis Caragiannis, Nisarg Shah, Alexandros A. Voudouris

We extend the recently introduced framework of metric distortion to multiwinner voting. In this framework, n agents and m alternatives are located in an underlying metric space…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.