Cellular Immunotherapy Treatment Scheduling to Address Antigen Escape. - Natalie Dullerud; Vanessa D. Jonsson (CDC, 2020)

An empirical framework for domain generalization in clinical settings. - Haoran Zhang 003; Natalie Dullerud; Laleh Seyyed-Kalantari; Quaid Morris; Shalmali Joshi; Marzyeh Ghassemi (CHIL, 2021)

Can You Fake It Until You Make It?: Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness. - Victoria Cheng; Vinith M. Suriyakumar; Natalie Dullerud; Shalmali Joshi; Marzyeh Ghassemi (FAccT, 2021)

CaPC Learning: Confidential and Private Collaborative Learning. - Christopher A. Choquette-Choo; Natalie Dullerud; Adam Dziedzic; Yunxiang Zhang 003; Somesh Jha; Nicolas Papernot; Xiao Wang 012 (ICLR, 2021)

Proof-of-Learning: Definitions and Practice. - Hengrui Jia 001; Mohammad Yaghini; Christopher A. Choquette-Choo; Natalie Dullerud; Anvith Thudi; Varun Chandrasekaran; Nicolas Papernot (SP, 2021)

Reading Race: AI Recognises Patient's Racial Identity In Medical Images. - Imon Banerjee; Ananth Reddy Bhimireddy; John L. Burns; Leo Anthony Celi; Li-Ching Chen; Ramon Correa; Natalie Dullerud; Marzyeh Ghassemi; Shih-Cheng Huang; Po-Chih Kuo; Matthew P. Lungren; Lyle J. Palmer; et al. (CoRR, 2021)

Improving the Fairness of Chest X-ray Classifiers. - Haoran Zhang 003; Natalie Dullerud; Karsten Roth; Lauren Oakden-Rayner; Stephen Pfohl; Marzyeh Ghassemi (CHIL, 2022)

Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning. - Natalie Dullerud; Karsten Roth; Kimia Hamidieh; Nicolas Papernot; Marzyeh Ghassemi (ICLR, 2022)

Washing The Unwashable : On The (Im)possibility of Fairwashing Detection. - Ali Shahin Shamsabadi; Mohammad Yaghini; Natalie Dullerud; Sierra Calanda Wyllie; Ulrich Aïvodji; Aisha Alaagib; Sébastien Gambs; Nicolas Papernot (NeurIPS, 2022)

On the Fundamental Limits of Formally (Dis)Proving Robustness in Proof-of-Learning. - Congyu Fang; Hengrui Jia 001; Anvith Thudi; Mohammad Yaghini; Christopher A. Choquette-Choo; Natalie Dullerud; Varun Chandrasekaran; Nicolas Papernot (CoRR, 2022)

Proof-of-Learning is Currently More Broken Than You Think. - Congyu Fang; Hengrui Jia 001; Anvith Thudi; Mohammad Yaghini; Christopher A. Choquette-Choo; Natalie Dullerud; Varun Chandrasekaran; Nicolas Papernot (EuroS&P, 2023)

Confidential-PROFITT: Confidential PROof of FaIr Training of Trees. - Ali Shahin Shamsabadi; Sierra Calanda Wyllie; Nicholas Franzese; Natalie Dullerud; Sébastien Gambs; Nicolas Papernot; Xiao Wang 012; Adrian Weller (ICLR, 2023)

Private Multi-Winner Voting for Machine Learning. - Adam Dziedzic; Christopher A. Choquette-Choo; Natalie Dullerud; Vinith M. Suriyakumar; Ali Shahin Shamsabadi; Muhammad Ahmad Kaleem; Somesh Jha; Nicolas Papernot; Xiao Wang 012 (Proc. Priv. Enhancing Technol., 2023)

MoSH: Modeling Multi-Objective Tradeoffs with Soft and Hard Bounds. - Edward Chen; Natalie Dullerud; Thomas Niedermayr; Elizabeth Kidd; Ransalu Senanayake; Pang Wei Koh; Sanmi Koyejo; Carlos Guestrin (CoRR, 2024)

Trustworthy ML Regulation as a Principal-Agent Problem. - Mohammad Yaghini; Patty Liu; Andrew Magnuson; Natalie Dullerud; Nicolas Papernot (FAccT, 2025)

Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds. - Edward Chen; Sang T. Truong; Natalie Dullerud; Sanmi Koyejo; Carlos Guestrin (CoRR, 2025)