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Posts by Collection
portfolio
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publications
Polarization in social media assists influencers to become more influential: analysis and two inoculation strategies
Published in Nature Scientific Reports, 2019
Abstract
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. https://doi.org/10.1038/s41598-019-55178-8
Review on learning and extracting graph features for link prediction
Published in Machine Learning and Knowledge Extraction, 2020
Abstract
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. https://doi.org/10.3390/make2040036
Exploring the disparity of influence between users in the discussion of Brexit on Twitter
Published in Journal of Computational Social Science, 2021
Abstract
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. Journal of Computational Social Science, 4, 903-917. https://doi.org/10.1007/s42001-021-00112-0
TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks
Published in Machine Learning and Knowledge Extraction, 2022
A Wasserstein GAN that first learns to generate realistic tabular data, then is fine-tuned to generate data that is also fair.
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. https://doi.org/10.3390/make4020022
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. https://doi.org/10.1007/978-3-031-35891-3_26
Through a Fair Looking-Glass: Mitigating Bias in Image Datasets
Published in International Conference on Human-Computer Interaction (HCII 2023), Artificial Intelligence in HCI, 2023
A fast way to de-bias an image dataset: a U-Net that reconstructs images while minimizing the statistical dependence between the target and protected attributes.
Recommended citation: Rajabi, A., Yazdani-Jahromi, M., Garibay, O. O., & Sukthankar, G. (2023). Through a fair looking-glass: mitigating bias in image datasets. In International Conference on Human-Computer Interaction (pp. 446-459). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-35891-3_27
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. https://proceedings.neurips.cc/paper_files/paper/2024/hash/bef7a072148e646fcb62641cc351e599-Abstract-Conference.html
talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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