Seth Neel

49 papers A* 12A 2B 1C 1Journal 28Unranked 5
YearRankTypeTitle / Venue / Authors
2025 J jnl
Efficiently Verifiable Proofs of Data Attribution.
CoRR
Ari Karchmer, Martin Pawelczyk, Seth Neel
2025 A* conf
Machine Unlearning Fails to Remove Data Poisoning Attacks.
ICLR
Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel
2025 A* conf
Machine Unlearning via Simulated Oracle Matching.
ICLR
Kristian Georgiev, Roy Rinberg, Sung Min Park, Shivam Garg, Andrew Ilyas, Aleksander Madry, Seth Neel
2025 J jnl
PRIMO: Private Regression in Multiple Outcomes.
Trans. Mach. Learn. Res.
Seth Neel
2024 J jnl
Attribute-to-Delete: Machine Unlearning via Datamodel Matching.
CoRR
Kristian Georgiev, Roy Rinberg, Sung Min Park, Shivam Garg, Andrew Ilyas, Aleksander Madry, Seth Neel
2024 A* conf
Feature Importance Disparities for Data Bias Investigations.
ICML
Peter W. Chang, Leor Fishman, Seth Neel
2024 A* conf
In-Context Unlearning: Language Models as Few-Shot Unlearners.
ICML
Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju
2024 J jnl
Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice.
CoRR
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, Peter Kairouz, Nicole Mitchell, Percy Liang, Daniel E. Ho, Yejin Choi, Sanmi Koyejo, Fernando A. Delgado, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Solon Barocas, Amy Cyphert, Mark A. Lemley, danah boyd, Jennifer Wortman Vaughan, Miles Brundage, David Bau, Seth Neel, Abigail Z. Jacobs, Andreas Terzis, Hanna M. Wallach, Nicolas Papernot, Katherine Lee
2024 J jnl
Machine Unlearning Fails to Remove Data Poisoning Attacks.
CoRR
Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel
2023 J jnl
Black-Box Training Data Identification in GANs via Detector Networks.
CoRR
Lukman Olagoke, Salil P. Vadhan, Seth Neel
2023 J jnl
In-Context Unlearning: Language Models as Few Shot Unlearners.
CoRR
Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju
2023 A* conf
MoPe: Model Perturbation based Privacy Attacks on Language Models.
EMNLP
Marvin Li, Jason Wang, Jeffrey G. Wang, Seth Neel
2023 J jnl
MoPe: Model Perturbation-based Privacy Attacks on Language Models.
CoRR
Marvin Li, Jason Wang, Jeffrey G. Wang, Seth Neel
2023 J jnl
Model Explanation Disparities as a Fairness Diagnostic.
CoRR
Peter W. Chang, Leor Fishman, Seth Neel
2023 A conf
On the Privacy Risks of Algorithmic Recourse.
AISTATS
Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel
2023 J jnl
PRIMO: Private Regression in Multiple Outcomes.
CoRR
Seth Neel
2023 J jnl
Privacy Issues in Large Language Models: A Survey.
CoRR
Seth Neel, Peter W. Chang
2022 J jnl
On the Privacy Risks of Algorithmic Recourse.
CoRR
Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel
2021 A* conf
A new analysis of differential privacy's generalization guarantees (invited paper).
STOC
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld
2021 A* conf
Adaptive Machine Unlearning.
NeurIPS
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites
2021 J jnl
Adaptive Machine Unlearning.
CoRR
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites
2021 — conf
An Algorithmic Framework for Fairness Elicitation.
FORC
Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu
2021 B conf
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning.
ALT
Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi
2020 A conf
A New Analysis of Differential Privacy's Generalization Guarantees.
ITCS
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld
2020 J jnl
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning.
CoRR
Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi
2020 — conf
Optimal, truthful, and private securities lending.
ICAIF
Emily Diana, Michael Kearns, Seth Neel, Aaron Roth
2020 A* conf
Oracle Efficient Private Non-Convex Optimization.
ICML
Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu
2019 J jnl
A New Analysis of Differential Privacy's Generalization Guarantees.
CoRR
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld
2019 J jnl
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM.
J. Priv. Confidentiality
Zhiwei Steven Wu, Aaron Roth, Katrina Ligett, Bo Waggoner, Seth Neel
2019 — conf
An Empirical Study of Rich Subgroup Fairness for Machine Learning.
FAT
Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
2019 J jnl
Differentially Private Objective Perturbation: Beyond Smoothness and Convexity.
CoRR
Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu
2019 J jnl
Eliciting and Enforcing Subjective Individual Fairness.
CoRR
Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu
2019 — conf
Fair Algorithms for Learning in Allocation Problems.
FAT
Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman
2019 A* conf
How to Use Heuristics for Differential Privacy.
FOCS
Seth Neel, Aaron Roth, Zhiwei Steven Wu
2019 J jnl
Optimal, Truthful, and Private Securities Lending.
CoRR
Emily Diana, Michael J. Kearns, Seth Neel, Aaron Roth
2019 A* conf
The Role of Interactivity in Local Differential Privacy.
FOCS
Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth
2019 J jnl
The Role of Interactivity in Local Differential Privacy.
CoRR
Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth
2018 J jnl
An Empirical Study of Rich Subgroup Fairness for Machine Learning.
CoRR
Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
2018 J jnl
Fair Algorithms for Learning in Allocation Problems.
CoRR
Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman
2018 J jnl
How to Use Heuristics for Differential Privacy.
CoRR
Seth Neel, Aaron Roth, Zhiwei Steven Wu
2018 C conf
Meritocratic Fairness for Infinite and Contextual Bandits.
AIES
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
2018 A* conf
Mitigating Bias in Adaptive Data Gathering via Differential Privacy.
ICML
Seth Neel, Aaron Roth
2018 J jnl
Mitigating Bias in Adaptive Data Gathering via Differential Privacy.
CoRR
Seth Neel, Aaron Roth
2018 A* conf
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness.
ICML
Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
2017 J jnl
A Convex Framework for Fair Regression.
CoRR
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
2017 — conf
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM.
NIPS
Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
2017 J jnl
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM.
CoRR
Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
2017 J jnl
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness.
CoRR
Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
2016 J jnl
Rawlsian Fairness for Machine Learning.
CoRR
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth