Publications

Fair Bilevel Neural Network (FairBiNN): On Balancing Fairness and Accuracy via Stackelberg Equilibrium

Published in Advances in Neural Information Processing Systems (NeurIPS 2024), 2024

This paper addresses the persistent challenge of bias in machine learning models, proposing a bilevel optimization approach that balances fairness and accuracy.

Recommended citation: Yazdani-Jahromi, M., Khodabandeh Yalabadi, A., Rajabi, A., Tayebi, A., Garibay, I., & Garibay, O. (2024). Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium. Advances in Neural Information Processing Systems, 37, 105780-105818.

Through a fair looking-glass: mitigating bias in image datasets

Published in International Conference on Human-Computer Interaction, 2023

Recommended citation: Rajabi, A., Yazdani-Jahromi, M., Garibay, O. O., & Sukthankar, G. (2023, July). Through a fair looking-glass: mitigating bias in image datasets. In International Conference on Human-Computer Interaction (pp. 446-459). Cham: Springer Nature Switzerland.

Distance Correlation GAN: Fair Tabular Data Generation with Generative Adversarial Networks

Published in International Conference on Human-Computer Interaction, 2023

Recommended citation: Rajabi, A., & Garibay, O. O. (2023, July). Distance Correlation GAN: Fair Tabular Data Generation with Generative Adversarial Networks. In International Conference on Human-Computer Interaction (pp. 431-445). Cham: Springer Nature Switzerland.

Distraction is all you need for fairness

Published in arXiv , 2022

Recommended citation: Yazdani-Jahromi, M., Rajabi, A., Tayebi, A., & Garibay, O. O. (2022). Distraction is all you need for fairness. arXiv preprint arXiv:2203.07593.

Tabfairgan: Fair tabular data generation with generative adversarial networks

Published in Machine Learning and Knowledge Extraction, 2022

Recommended citation: Rajabi, A., & Garibay, O. O. (2022). Tabfairgan: Fair tabular data generation with generative adversarial networks. Machine Learning and Knowledge Extraction, 4(2), 488-501.

Exploring the disparity of influence between users in the discussion of brexit on twitter: Twitter influence disparity in brexit if so, write it here

Published in Journal of Computational Social Science, 2021

Recommended citation: Rajabi, A., Mantzaris, A. V., Atwal, K. S., & Garibay, I. (2021). Exploring the disparity of influence between users in the discussion of brexit on twitter: Twitter influence disparity in brexit if so, write it here. Journal of Computational Social Science, 4, 903-917.

Review on learning and extracting graph features for link prediction

Published in Machine Learning and Knowledge Extraction, 2020

Recommended citation: Mutlu, E. C., Oghaz, T., Rajabi, A., & Garibay, I. (2020). Review on learning and extracting graph features for link prediction. Machine Learning and Knowledge Extraction, 2(4), 672-704.

Polarization in social media assists influencers to become more influential: analysis and two inoculation strategies

Published in Nature Scientific Reports, 2019

Recommended citation: Garibay, I., Mantzaris, A. V., Rajabi, A., & Taylor, C. E. (2019). Polarization in social media assists influencers to become more influential: analysis and two inoculation strategies. Scientific reports, 9(1), 18592.