Seth Neel

According to our database, Seth Neel authored at least 29 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

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 Fails to Remove Data Poisoning Attacks.

Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel

CoRR, 2024

In-Context Unlearning: Language Models as Few-Shot Unlearners.

Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Feature Importance Disparities for Data Bias Investigations.

Peter W. Chang, Leor Fishman, Seth Neel

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Privacy Issues in Large Language Models: A Survey.

Seth Neel, Peter W. Chang

CoRR, 2023

Black-Box Training Data Identification in GANs via Detector Networks.

Lukman Olagoke, Salil P. Vadhan, Seth Neel

CoRR, 2023

PRIMO: Private Regression in Multiple Outcomes.

Seth Neel

CoRR, 2023

Model Explanation Disparities as a Fairness Diagnostic.

Peter W. Chang, Leor Fishman, Seth Neel

CoRR, 2023

MoPe: Model Perturbation based Privacy Attacks on Language Models.

Marvin Li, Jason Wang, Jeffrey G. Wang, Seth Neel

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

On the Privacy Risks of Algorithmic Recourse.

Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2021

A new analysis of differential privacy's generalization guarantees (invited paper).

Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld

Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

Adaptive Machine Unlearning.

Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

An Algorithmic Framework for Fairness Elicitation.

Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu

Proceedings of the 2nd Symposium on Foundations of Responsible Computing, 2021

Descent-to-Delete: Gradient-Based Methods for Machine Unlearning.

Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi

Proceedings of the Algorithmic Learning Theory, 2021

2020

A New Analysis of Differential Privacy's Generalization Guarantees.

Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld

Proceedings of the 11th Innovations in Theoretical Computer Science Conference, 2020

Oracle Efficient Private Non-Convex Optimization.

Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu

Proceedings of the 37th International Conference on Machine Learning, 2020

Optimal, truthful, and private securities lending.

Emily Diana, Michael Kearns, Seth Neel, Aaron Roth

Proceedings of the ICAIF '20: The First ACM International Conference on AI in Finance, 2020

2019

Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM.

Zhiwei Steven Wu, Aaron Roth, Katrina Ligett, Bo Waggoner, Seth Neel

J. Priv. Confidentiality, 2019

Differentially Private Objective Perturbation: Beyond Smoothness and Convexity.

Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu

CoRR, 2019

Eliciting and Enforcing Subjective Individual Fairness.

Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu

CoRR, 2019

How to Use Heuristics for Differential Privacy.

Seth Neel, Aaron Roth, Zhiwei Steven Wu

Proceedings of the 60th IEEE Annual Symposium on Foundations of Computer Science, 2019

The Role of Interactivity in Local Differential Privacy.

Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth

Proceedings of the 60th IEEE Annual Symposium on Foundations of Computer Science, 2019

An Empirical Study of Rich Subgroup Fairness for Machine Learning.

Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu

Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

Fair Algorithms for Learning in Allocation Problems.

Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman

Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

2018

Mitigating Bias in Adaptive Data Gathering via Differential Privacy.

Seth Neel, Aaron Roth

Proceedings of the 35th International Conference on Machine Learning, 2018

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness.

Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu

Proceedings of the 35th International Conference on Machine Learning, 2018

Meritocratic Fairness for Infinite and Contextual Bandits.

Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth

Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018

2017

A Convex Framework for Fair Regression.

Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth

CoRR, 2017

2016

Rawlsian Fairness for Machine Learning.

Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth

CoRR, 2016