A Practical Study of Longitudinal Reference Based Compressed Sensing for MRI. - Samuel Birns; Bohyun Kim; Stephanie Ku; Kevin Stangl; Deanna Needell (CoRR, 2016)

Recovering from Biased Data: Can Fairness Constraints Improve Accuracy? - Avrim Blum; Kevin Stangl (FORC, 2020)

Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a Pre-Existing Pipeline. - Avrim Blum; Kevin Stangl; Ali Vakilian (FAccT, 2022)

Sequential Strategic Screening. - Lee Cohen 001; Saeed Sharifi-Malvajerdi; Kevin Stangl; Ali Vakilian; Juba Ziani (ICML, 2023)

Certifiable (Multi)Robustness Against Patch Attacks Using ERM. - Saba Ahmadi; Avrim Blum; Omar Montasser; Kevin Stangl (CoRR, 2023)

Agnostic Multi-Robust Learning using ERM. - Saba Ahmadi; Avrim Blum; Omar Montasser; Kevin M. Stangl (AISTATS, 2024)

On the Vulnerability of Fairness Constrained Learning to Malicious Noise. - Avrim Blum; Princewill Okoroafor; Aadirupa Saha; Kevin M. Stangl (AISTATS, 2024)

Bayesian Strategic Classification. - Lee Cohen 001; Saeed Sharifi-Malvajerdi; Kevin Stangl; Ali Vakilian; Juba Ziani (NeurIPS, 2024)

Investigating the Semantic Robustness of CLIP-based Zero-Shot Anomaly Segmentation. - Kevin Stangl; Marius Arvinte; Weilin Xu; Cory Cornelius (CoRR, 2024)

Fairness, Accuracy, and Unreliable Data. - Kevin Stangl (CoRR, 2024)

Keep on Swimming: Real Attackers Only Need Partial Knowledge of a Multi-Model System. - Julian Collado; Kevin Stangl (CoRR, 2024)