Rawlsian Fairness for Machine Learning. - Matthew Joseph; Michael J. Kearns; Jamie Morgenstern; Seth Neel; Aaron Roth 001 (CoRR, 2016)

A Convex Framework for Fair Regression. - Richard Berk; Hoda Heidari; Shahin Jabbari; Matthew Joseph; Michael J. Kearns; Jamie Morgenstern; Seth Neel; Aaron Roth 001 (CoRR, 2017)

Meritocratic Fairness for Infinite and Contextual Bandits. - Matthew Joseph; Michael J. Kearns; Jamie Morgenstern; Seth Neel; Aaron Roth 001 (AIES, 2018)

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. - Michael J. Kearns; Seth Neel; Aaron Roth 001; Zhiwei Steven Wu (ICML, 2018)

Mitigating Bias in Adaptive Data Gathering via Differential Privacy. - Seth Neel; Aaron Roth 001 (ICML, 2018)

Fair Algorithms for Learning in Allocation Problems. - Hadi Elzayn; Shahin Jabbari; Christopher Jung 001; Michael J. Kearns; Seth Neel; Aaron Roth 001; Zachary Schutzman (FAT, 2019)

An Empirical Study of Rich Subgroup Fairness for Machine Learning. - Michael J. Kearns; Seth Neel; Aaron Roth 001; Zhiwei Steven Wu (FAT, 2019)

The Role of Interactivity in Local Differential Privacy. - Matthew Joseph; Jieming Mao; Seth Neel; Aaron Roth 001 (FOCS, 2019)

How to Use Heuristics for Differential Privacy. - Seth Neel; Aaron Roth 001; Zhiwei Steven Wu (FOCS, 2019)

Eliciting and Enforcing Subjective Individual Fairness. - Christopher Jung 001; Michael J. Kearns; Seth Neel; Aaron Roth 001; Logan Stapleton; Zhiwei Steven Wu (CoRR, 2019)

Differentially Private Objective Perturbation: Beyond Smoothness and Convexity. - Seth Neel; Aaron Roth 001; Giuseppe Vietri; Zhiwei Steven Wu (CoRR, 2019)

Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM. - Zhiwei Steven Wu; Aaron Roth 001; Katrina Ligett; Bo Waggoner; Seth Neel (J. Priv. Confidentiality, 2019)

Optimal, truthful, and private securities lending. - Emily Diana; Michael Kearns; Seth Neel; Aaron Roth 001 (ICAIF, 2020)

Oracle Efficient Private Non-Convex Optimization. - Seth Neel; Aaron Roth 001; Giuseppe Vietri; Zhiwei Steven Wu (ICML, 2020)

A New Analysis of Differential Privacy's Generalization Guarantees. - Christopher Jung 001; Katrina Ligett; Seth Neel; Aaron Roth 001; Saeed Sharifi-Malvajerdi; Moshe Shenfeld (ITCS, 2020)

Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. - Seth Neel; Aaron Roth 001; Saeed Sharifi-Malvajerdi (ALT, 2021)

An Algorithmic Framework for Fairness Elicitation. - Christopher Jung 001; Michael Kearns; Seth Neel; Aaron Roth 001; Logan Stapleton; Zhiwei Steven Wu (FORC, 2021)

Adaptive Machine Unlearning. - Varun Gupta 006; Christopher Jung 001; Seth Neel; Aaron Roth 001; Saeed Sharifi-Malvajerdi; Chris Waites (NeurIPS, 2021)

A new analysis of differential privacy's generalization guarantees (invited paper). - Christopher Jung 001; Katrina Ligett; Seth Neel; Aaron Roth 001; Saeed Sharifi-Malvajerdi; Moshe Shenfeld (STOC, 2021)

On the Privacy Risks of Algorithmic Recourse. - Martin Pawelczyk; Himabindu Lakkaraju; Seth Neel (AISTATS, 2023)

MoPe: Model Perturbation based Privacy Attacks on Language Models. - Marvin Li; Jason Wang; Jeffrey G. Wang; Seth Neel (EMNLP, 2023)

Model Explanation Disparities as a Fairness Diagnostic. - Peter W. Chang; Leor Fishman; Seth Neel (CoRR, 2023)

Black-Box Training Data Identification in GANs via Detector Networks. - Lukman Olagoke; Salil P. Vadhan; Seth Neel (CoRR, 2023)

Privacy Issues in Large Language Models: A Survey. - Seth Neel; Peter W. Chang (CoRR, 2023)

Feature Importance Disparities for Data Bias Investigations. - Peter W. Chang; Leor Fishman; Seth Neel (ICML, 2024)

In-Context Unlearning: Language Models as Few-Shot Unlearners. - Martin Pawelczyk; Seth Neel; Himabindu Lakkaraju (ICML, 2024)

Attribute-to-Delete: Machine Unlearning via Datamodel Matching. - Kristian Georgiev; Roy Rinberg; Sung Min Park; Shivam Garg; Andrew Ilyas; Aleksander Madry; Seth Neel (CoRR, 2024)

Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice. - A. Feder Cooper; Christopher A. Choquette-Choo; Miranda Bogen; Matthew Jagielski; Katja Filippova; Ken Ziyu Liu; Alexandra Chouldechova; Jamie Hayes; Yangsibo Huang; Niloofar Mireshghallah; Ilia Shumailov; Eleni Triantafillou; et al. (CoRR, 2024)

Machine Unlearning via Simulated Oracle Matching. - Kristian Georgiev; Roy Rinberg; Sung Min Park; Shivam Garg; Andrew Ilyas; Aleksander Madry; Seth Neel (ICLR, 2025)

Machine Unlearning Fails to Remove Data Poisoning Attacks. - Martin Pawelczyk; Jimmy Z. Di; Yiwei Lu 001; Gautam Kamath 001; Ayush Sekhari; Seth Neel (ICLR, 2025)

PRIMO: Private Regression in Multiple Outcomes. - Seth Neel (Trans. Mach. Learn. Res., 2025)

Efficiently Verifiable Proofs of Data Attribution. - Ari Karchmer; Martin Pawelczyk; Seth Neel (CoRR, 2025)