Golnoosh Farnadi

Aly Kassem

Collaborateur·rice de recherche - UWindsor

Doctorat - McGill

Co-superviseur⋅e :

Siva Reddy

Google Scholar

Saber Malekmohammadi

Collaborateur·rice de recherche - McGill

Yash Raj More

Collaborateur·rice alumni - UdeM

Selin Omur

Collaborateur·rice de recherche - McGill

Sophia Osborne

Stagiaire de recherche - McGill

Edie Pearman

Visiteur de recherche indépendant - McGill university

Nahian Rahman

Stagiaire de recherche - McGill

Ameline Ramesan

Collaborateur·rice de recherche - McGill

Soumya Sharma

Doctorat - McGill

Co-superviseur⋅e :

Adriana Romero Soriano

Site web

Google Scholar

Zhuan Shi

Postdoctorat - McGill

William St-Arnaud

Doctorat - UdeM

Co-superviseur⋅e :

Margarida Carvalho

Emma Tomiuk Tomiuk

Maîtrise recherche - McGill

Publications

A taxonomy of weight learning methods for statistical relational learning

Sriram Srinivasan

Charles Dickens

Eriq Augustine

Lise Getoor

2021-12-13

Machine-mediated learning (publié)

A taxonomy of weight learning methods for statistical relational learning

Sriram Srinivasan

Charles Dickens

Eriq Augustine

Lise Getoor

2021-12-13

Machine-mediated learning (published)

Individual Fairness in Kidney Exchange Programs

2021-05-18

AAAI Conference on Artificial Intelligence (publié)

A Unifying Framework for Fairness-Aware Influence Maximization

Behrouz Babaki

Michel Gendreau

The problem of selecting a subset of nodes with greatest influence in a graph, commonly known as influence maximization, has been well studi… (voir plus)ed over the past decade. This problem has real world applications which can potentially affect lives of individuals. Algorithmic decision making in such domains raises concerns about their societal implications. One of these concerns, which surprisingly has only received limited attention so far, is algorithmic bias and fairness. We propose a flexible framework that extends and unifies the existing works in fairness-aware influence maximization. This framework is based on an integer programming formulation of the influence maximization problem. The fairness requirements are enforced by adding linear constraints or modifying the objective function. Contrary to the previous work which designs specific algorithms for each variant, we develop a formalism which is general enough for specifying different notions of fairness. A problem defined in this formalism can be then solved using efficient mixed integer programming solvers. The experimental evaluation indicates that our framework not only is general but also is competitive with existing algorithms.

2020-04-20

Companion Proceedings of the Web Conference 2020 (publié)

Fairness in Kidney Exchange Programs through Optimal Solutions Enumeration