Deep-Learning-Based Blind Recognition of Channel Code Parameters Over Candidate Sets Under AWGN and Multi-Path Fading Conditions. - Sepehr Dehdashtian; Matin Hashemi; Saber Salehkaleybar (IEEE Wirel. Commun. Lett., 2021)

On Characterizing the Trade-off in Invariant Representation Learning. - Bashir Sadeghi; Sepehr Dehdashtian; Vishnu Boddeti (Trans. Mach. Learn. Res., 2022)

Utility-Fairness Trade-Offs and how to Find Them. - Sepehr Dehdashtian; Bashir Sadeghi; Vishnu Naresh Boddeti (CVPR, 2024)

The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models. - Abeba Birhane; Sepehr Dehdashtian; Vinay Uday Prabhu; Vishnu Boddeti (FAccT, 2024)

FairerCLIP: Debiasing CLIP's Zero-Shot Predictions using Functions in RKHSs. - Sepehr Dehdashtian; Lan Wang; Vishnu Boddeti (ICLR, 2024)

Fairness and Bias Mitigation in Computer Vision: A Survey. - Sepehr Dehdashtian; Ruozhen He; Yi Li 051; Guha Balakrishnan; Nuno Vasconcelos; Vicente Ordonez; Vishnu Naresh Boddeti (CoRR, 2024)

OASIS Uncovers: High-Quality T2I Models, Same Old Stereotypes. - Sepehr Dehdashtian; Gautam Sreekumar; Vishnu Boddeti (ICLR, 2025)