Neel, Seth

15 publications

NeurIPS 2025 Efficiently Verifiable Proofs of Data Attribution Ari Karchmer, Seth Neel, Martin Pawelczyk

NeurIPS 2025 Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Kevin Klyman, Matthew Jagielski, Katja Filippova, Ken Liu, Alexandra Chouldechova, Jamie Hayes, Yangsibo Huang, Eleni Triantafillou, Peter Kairouz, Nicole Elyse Mitchell, Niloofar Mireshghallah, Abigail Z. Jacobs, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Ilia Shumailov, Andreas Terzis, Solon Barocas, Jennifer Wortman Vaughan, Danah Boyd, Yejin Choi, Sanmi Koyejo, Fernando Delgado, Percy Liang, Daniel E. Ho, Pamela Samuelson, Miles Brundage, David Bau, Seth Neel, Hanna Wallach, Amy B. Cyphert, Mark Lemley, Nicolas Papernot, Katherine Lee

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

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

TMLR 2025 PRIMO: Private Regression in Multiple Outcomes Seth Neel

ICML 2024 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

NeurIPSW 2023 MoPe: Model Perturbation-Based Privacy Attacks on Language Models Jason Wang, Jeffrey Wang, Marvin Li, Seth Neel

AISTATS 2023 On the Privacy Risks of Algorithmic Recourse Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel

NeurIPS 2021 Adaptive Machine Unlearning Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites

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

ICML 2020 Oracle Efficient Private Non-Convex Optimization Seth Neel, Aaron Roth, Giuseppe Vietri, Steven Wu

ICML 2018 Mitigating Bias in Adaptive Data Gathering via Differential Privacy Seth Neel, Aaron Roth

ICML 2018 Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu

NeurIPS 2017 Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Steven Z. Wu