Seth Neel

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 41

Top Topics

Differential Privacy

Differentially Private

Convex Optimization

Top Venues

Publications

Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel
Machine Unlearning Fails to Remove Data Poisoning Attacks. CoRR (2024)
Lukman Olagoke, Salil Vadhan, Seth Neel
Black-Box Training Data Identification in GANs via Detector Networks. CoRR (2023)
Marvin Li, Jason Wang, Jeffrey Wang, Seth Neel
MoPe: Model Perturbation based Privacy Attacks on Language Models. EMNLP (2023)
Seth Neel
PRIMO: Private Regression in Multiple Outcomes. CoRR (2023)
Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel
On the Privacy Risks of Algorithmic Recourse. AISTATS (2023)
Peter W. Chang, Leor Fishman, Seth Neel
Model Explanation Disparities as a Fairness Diagnostic. CoRR (2023)
Marvin Li, Jason Wang, Jeffrey Wang, Seth Neel
MoPe: Model Perturbation-based Privacy Attacks on Language Models. CoRR (2023)
Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju
In-Context Unlearning: Language Models as Few Shot Unlearners. CoRR (2023)
Seth Neel, Peter W. Chang
Privacy Issues in Large Language Models: A Survey. CoRR (2023)
Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel
On the Privacy Risks of Algorithmic Recourse. CoRR (2022)
Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. ALT (2021)
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld
A new analysis of differential privacy's generalization guarantees (invited paper). STOC (2021)
Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu
An Algorithmic Framework for Fairness Elicitation. FORC (2021)
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites
Adaptive Machine Unlearning. NeurIPS (2021)
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites
Adaptive Machine Unlearning. CoRR (2021)
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld
A New Analysis of Differential Privacy's Generalization Guarantees. ITCS (2020)
Emily Diana, Michael Kearns, Seth Neel, Aaron Roth
Optimal, truthful, and private securities lending. ICAIF (2020)
Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. CoRR (2020)
Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu
Oracle Efficient Private Non-Convex Optimization. ICML (2020)
Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu
Differentially Private Objective Perturbation: Beyond Smoothness and Convexity. CoRR (2019)
Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth
The Role of Interactivity in Local Differential Privacy. CoRR (2019)
Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth
The Role of Interactivity in Local Differential Privacy. FOCS (2019)
Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
An Empirical Study of Rich Subgroup Fairness for Machine Learning. FAT (2019)
Zhiwei Steven Wu, Aaron Roth, Katrina Ligett, Bo Waggoner, Seth Neel
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM. J. Priv. Confidentiality 9 (2) (2019)
Seth Neel, Aaron Roth, Zhiwei Steven Wu
How to Use Heuristics for Differential Privacy. FOCS (2019)
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld
A New Analysis of Differential Privacy's Generalization Guarantees. CoRR (2019)
Emily Diana, Michael J. Kearns, Seth Neel, Aaron Roth
Optimal, Truthful, and Private Securities Lending. CoRR (2019)
Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman
Fair Algorithms for Learning in Allocation Problems. FAT (2019)
Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu
Eliciting and Enforcing Subjective Individual Fairness. CoRR (2019)
Seth Neel, Aaron Roth, Zhiwei Steven Wu
How to Use Heuristics for Differential Privacy. CoRR (2018)
Seth Neel, Aaron Roth
Mitigating Bias in Adaptive Data Gathering via Differential Privacy. ICML (2018)
Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
An Empirical Study of Rich Subgroup Fairness for Machine Learning. CoRR (2018)
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
Meritocratic Fairness for Infinite and Contextual Bandits. AIES (2018)
Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. ICML (2018)
Seth Neel, Aaron Roth
Mitigating Bias in Adaptive Data Gathering via Differential Privacy. CoRR (2018)
Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman
Fair Algorithms for Learning in Allocation Problems. CoRR (2018)
Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. CoRR (2017)
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
A Convex Framework for Fair Regression. CoRR (2017)
Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM. NIPS (2017)
Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM. CoRR (2017)
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
Rawlsian Fairness for Machine Learning. CoRR (2016)