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ORCIDSepehr Dehdashtian
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2020 – today
- 2025
[c4]Sepehr Dehdashtian, Gautam Sreekumar, Vishnu Boddeti:
OASIS Uncovers: High-Quality T2I Models, Same Old Stereotypes. ICLR 2025- [i7]Sepehr Dehdashtian, Gautam Sreekumar, Vishnu Naresh Boddeti:
OASIS Uncovers: High-Quality T2I Models, Same Old Stereotypes. CoRR abs/2501.00962 (2025) - [i6]Sepehr Dehdashtian, Mashrur M. Morshed, Jacob H. Seidman, Gaurav Bharaj, Vishnu Naresh Boddeti:
PolyJuice Makes It Real: Black-Box, Universal Red Teaming for Synthetic Image Detectors. CoRR abs/2509.15551 (2025) - 2024
- [c3]Sepehr Dehdashtian, Bashir Sadeghi, Vishnu Naresh Boddeti:
Utility-Fairness Trade-Offs and how to Find Them. CVPR 2024: 12038-12046 - [c2]Abeba Birhane
, Sepehr Dehdashtian, Vinay Uday Prabhu, Vishnu Boddeti:
The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models. FAccT 2024: 1229-1244 - [c1]Sepehr Dehdashtian, Lan Wang, Vishnu Boddeti:
FairerCLIP: Debiasing CLIP's Zero-Shot Predictions using Functions in RKHSs. ICLR 2024 - [i5]Sepehr Dehdashtian, Lan Wang, Vishnu Naresh Boddeti:
FairerCLIP: Debiasing CLIP's Zero-Shot Predictions using Functions in RKHSs. CoRR abs/2403.15593 (2024) - [i4]Sepehr Dehdashtian, Bashir Sadeghi, Vishnu Naresh Boddeti:
Utility-Fairness Trade-Offs and How to Find Them. CoRR abs/2404.09454 (2024) - [i3]Abeba Birhane, Sepehr Dehdashtian, Vinay Uday Prabhu, Vishnu Boddeti:
The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models. CoRR abs/2405.04623 (2024) - [i2]Sepehr Dehdashtian, Ruozhen He, Yi Li, Guha Balakrishnan, Nuno Vasconcelos, Vicente Ordonez, Vishnu Naresh Boddeti:
Fairness and Bias Mitigation in Computer Vision: A Survey. CoRR abs/2408.02464 (2024) - 2022
- [j2]Bashir Sadeghi, Sepehr Dehdashtian, Vishnu Boddeti:
On Characterizing the Trade-off in Invariant Representation Learning. Trans. Mach. Learn. Res. 2022 (2022) - 2021
- [j1]Sepehr Dehdashtian, Matin Hashemi, Saber Salehkaleybar:
Deep-Learning-Based Blind Recognition of Channel Code Parameters Over Candidate Sets Under AWGN and Multi-Path Fading Conditions. IEEE Wirel. Commun. Lett. 10(5): 1041-1045 (2021) - 2020
- [i1]Sepehr Dehdashtian, Matin Hashemi, Saber Salehkaleybar:
Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions. CoRR abs/2009.07774 (2020)
Coauthor Index
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