2 citations · 5 across the 6 of their papers we have counts for
8 papers
Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium
Mehdi Yazdani-Jahromi, Ali Khodabandeh Yalabadi, AmirArsalan Rajabi +3
The persistent challenge of bias in machine learning models necessitates robust solutions to ensure parity and equal treatment across diverse groups, particularly in classification…
Through a fair looking-glass: mitigating bias in image datasets
Amirarsalan Rajabi, Mehdi Yazdani-Jahromi, Ozlem Ozmen Garibay +1
With the recent growth in computer vision applications, the question of how fair and unbiased they are has yet to be explored. There is abundant evidence that the bias present in t…
Distraction is All You Need for Fairness
Mehdi Yazdani-Jahromi, AmirArsalan Rajabi, Ali Khodabandeh Yalabadi +2
Bias in training datasets must be managed for various groups in classification tasks to ensure parity or equal treatment. With the recent growth in artificial intelligence models a…
TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks
Amirarsalan Rajabi, Ozlem Ozmen Garibay
With the increasing reliance on automated decision making, the issue of algorithmic fairness has gained increasing importance. In this paper, we propose a Generative Adversarial Ne…
A Stance Data Set on Polarized Conversations on Twitter about the Efficacy of Hydroxychloroquine as a Treatment for COVID-19
Ece Çiğdem Mutlu, Toktam A. Oghaz, Jasser Jasser +5
At the time of this study, the SARS-CoV-2 virus that caused the COVID-19 pandemic has spread significantly across the world. Considering the uncertainty about policies, health risk…
CD-SEIZ: Cognition-Driven SEIZ Compartmental Model for the Prediction of Information Cascades on Twitter
Ece Çiğdem Mutlu, Amirarsalan Rajabi, Ivan Garibay
Information spreading social media platforms has become ubiquitous in our lives due to viral information propagation regardless of its veracity. Some information cascades turn out…