2 citations · 7 across the 9 of their papers we have counts for
5 papers · 1 filter
FairContrast: Enhancing Fairness through Contrastive learning and Customized Augmenting Methods on Tabular Data
Aida Tayebi, Ali Khodabandeh Yalabadi, Mehdi Yazdani-Jahromi +1
As AI systems become more embedded in everyday life, the development of fair and unbiased models becomes more critical. Considering the social impact of AI systems is not merely a…
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…
FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation
Ali Khodabandeh Yalabadi, Mehdi Yazdani-Jahromi, Niloofar Yousefi +3
Drug-Target Interaction (DTI) prediction is vital for drug discovery, yet challenges persist in achieving model interpretability and optimizing performance. We propose a novel tran…
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…