@inproceedings{dehdashtian2025oasis,title={OASIS Uncovers: High-Quality T2I Models, Same Old Stereotypes},author={Dehdashtian, Sepehr and Sreekumar, Gautam and Boddeti, Vishnu Naresh},booktitle={International Conference on Learning Representations (ICLR)},year={2025},month=may,url={https://openreview.net/forum?id=L6IgkJvcgV},dimensions={true},}
2024
Preprint
Fairness and Bias Mitigation in Computer Vision: A Survey
Sepehr Dehdashtian*, Ruozhen He*, Yi Li, Guha Balakrishnan, Nuno Vasconcelos, Vicente Ordonez, and Vishnu Naresh Boddeti
@misc{dehdashtian2024fairnessbiasmitigationcomputer,title={Fairness and Bias Mitigation in Computer Vision: A Survey},author={Dehdashtian*, Sepehr and He*, Ruozhen and Li, Yi and Balakrishnan, Guha and Vasconcelos, Nuno and Ordonez, Vicente and Boddeti, Vishnu Naresh},year={2024},eprint={2408.02464},archiveprefix={arXiv},primaryclass={cs.CV},url={https://arxiv.org/abs/2408.02464},month=jul,dimensions={true},}
FAccT’24
The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models
Abeba Birhane*, Sepehr Dehdashtian*, Vinay Uday Prabhu, and Vishnu Boddeti
In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency , Jul 2024
@inproceedings{birhane2024thedark,title={The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models},author={Birhane*, Abeba and Dehdashtian*, Sepehr and Prabhu, Vinay Uday and Boddeti, Vishnu},booktitle={Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency},year={2024},month=jul,dimensions={true},}
CVPR’24
Utility-Fairness Trade-Offs and How to Find Them
Sepehr Dehdashtian, Bashir Sadeghi, and Vishnu Boddeti
In Conference on Computer Vision and Pattern Recognition (CVPR) 2024 , Jun 2024
@inproceedings{dehdashtian2024utilityfairness,title={Utility-Fairness Trade-Offs and How to Find Them},author={Dehdashtian, Sepehr and Sadeghi, Bashir and Boddeti, Vishnu},booktitle={Conference on Computer Vision and Pattern Recognition (CVPR) 2024},year={2024},month=jun,dimensions={true},}
ICLR’24
FairerCLIP: Debiasing CLIP’s Zero-Shot Predictions using Functions in RKHSs
Sepehr Dehdashtian*, Lan Wang*, and Vishnu Boddeti
In International Conference on Learning Representations (ICLR) , May 2024
@inproceedings{dehdashtian2024fairvlm,title={FairerCLIP: Debiasing CLIP’s Zero-Shot Predictions using Functions in RKHSs},author={Dehdashtian*, Sepehr and Wang*, Lan and Boddeti, Vishnu},booktitle={International Conference on Learning Representations (ICLR)},year={2024},month=may,url={https://openreview.net/forum?id=HXoq9EqR9e},dimensions={true},}
2022
TMLR
On Characterizing the Trade-off in Invariant Representation Learning
Bashir Sadeghi, Sepehr Dehdashtian, and Vishnu Boddeti
Transactions on Machine Learning Research (TMLR), May 2022
@article{sadeghi2022on,title={On Characterizing the Trade-off in Invariant Representation Learning},author={Sadeghi, Bashir and Dehdashtian, Sepehr and Boddeti, Vishnu},journal={Transactions on Machine Learning Research (TMLR)},issn={2835-8856},year={2022},url={https://openreview.net/forum?id=3gfpBR1ncr},note={Featured Certification},dimensions={true},}
2021
WCL
Deep-Learning-Based Blind Recognition of Channel Code Parameters Over Candidate Sets Under AWGN and Multi-Path Fading Conditions
Sepehr Dehdashtian, Matin Hashemi, and Saber Salehkaleybar
@article{9344644,author={Dehdashtian, Sepehr and Hashemi, Matin and Salehkaleybar, Saber},journal={IEEE Wireless Communications Letters},title={Deep-Learning-Based Blind Recognition of Channel Code Parameters Over Candidate Sets Under AWGN and Multi-Path Fading Conditions},year={2021},volume={10},number={5},pages={1041-1045},dimensions={true},}