Publications

Selected

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

Mehdi Yazdani-Jahromi, Ali Khodabandeh Yalabadi, Amirarsalan Rajabi, Aida Tayebi, Ivan Garibay, Ozlem Ozmen Garibay

NeurIPS 2024

Fairness and accuracy are treated as two players in a Stackelberg game, each owning its own layers of one network. The bilevel solution is provably no worse than the usual Lagrangian (regularization) approach, and in practice it reaches a better accuracy–fairness trade-off.

Paper arXiv Code BibTeX

Through a Fair Looking-Glass: Mitigating Bias in Image Datasets

Amirarsalan Rajabi, Mehdi Yazdani-Jahromi, Ozlem Ozmen Garibay, Gita Sukthankar

HCII 2023

A U-Net learns small edits to each image that reduce the statistical dependence (HSIC) between the target attribute (Attractive) and gender. This de-biases the dataset itself, so classifiers trained on it are fairer.

Paper arXiv BibTeX

TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks

Amirarsalan Rajabi, Ozlem Ozmen Garibay

MAKE 2022

A Wasserstein GAN trained in two phases: first to reproduce the joint distribution of a tabular dataset, then with a fairness term added to the generator loss. The result is synthetic data with demographic parity nearly removed and utility largely kept.

Paper arXiv Code BibTeX

All publications

• Fair Bilevel Neural Network (FairBiNN): On Balancing Fairness and Accuracy via Stackelberg Equilibrium Mehdi Yazdani-Jahromi, Ali Khodabandeh Yalabadi, Amirarsalan Rajabi, Aida Tayebi, Ivan Garibay, Ozlem Ozmen Garibay
  NeurIPS 2024
• Through a Fair Looking-Glass: Mitigating Bias in Image Datasets Amirarsalan Rajabi, Mehdi Yazdani-Jahromi, Ozlem Ozmen Garibay, Gita Sukthankar
  HCII 2023
• Distance Correlation GAN: Fair Tabular Data Generation with Generative Adversarial Networks Amirarsalan Rajabi, Ozlem Ozmen Garibay
  HCII 2023
• TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks Amirarsalan Rajabi, Ozlem Ozmen Garibay
  MAKE 2022
• Exploring the disparity of influence between users in the discussion of Brexit on Twitter Amirarsalan Rajabi, Alexander V. Mantzaris, Kuldip Singh Atwal, Ivan Garibay
  J. Comput. Soc. Sci. 2021
• Review on learning and extracting graph features for link prediction Ece C. Mutlu, Toktam Oghaz, Amirarsalan Rajabi, Ivan Garibay
  MAKE 2020
• Polarization in social media assists influencers to become more influential: analysis and two inoculation strategies Ivan Garibay, Alexander V. Mantzaris, Amirarsalan Rajabi, Cameron E. Taylor
  Nature Scientific Reports 2019