skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "Akbari, Sina"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; (, Omega)
    Full Text Available 
  2. ; (, Computers & Operations Research)
    Full Text Available 
  3. ; (, International Symposium on Combinatorial Optimization)
    Top-k lists are being increasingly utilized in various fields and applications including information retrieval, machine learning, and recommendation systems. Since multiple top-k lists may be generated by different algorithms to evaluate the same set of entities or system of interest, there is often a need to consolidate this collection of heterogeneous top-k lists to obtain a more robust and coherent list. This work introduces various exact mathematical formulations of the top-k list aggregation problem under the generalized Kendall tau distance. Furthermore, the strength of the proposed formulations is analyzed from a polyhedral point of view. 
    more » « less
    Accepted Manuscript Full Text Available
  4. ; (, 2021 IEEE Symposium Series on Computational Intelligence)
    Rank aggregation has many applications in computer science, operations research, and group decision-making. This paper introduces lower bounds on the Kemeny aggregation problem when the input rankings are non-strict (with and without ties). It generalizes some of the existing lower bounds for strict rankings to the case of non-strict rankings, and it proposes shortcuts for reducing the run time of these techniques. More specifically, we use Condorcet criterion variations and the Branch & Cut method to accelerate the lower bounding process. 
    more » « less
    Full Text Available