Seth Neel

According to our database1, Seth Neel authored at least 19 papers between 2016 and 2021.

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Bibliography

2021
A new analysis of differential privacy's generalization guarantees (invited paper).
Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

Adaptive Machine Unlearning.
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.
Proceedings of the 2nd Symposium on Foundations of Responsible Computing, 2021

Descent-to-Delete: Gradient-Based Methods for Machine Unlearning.
Proceedings of the Algorithmic Learning Theory, 2021

2020
A New Analysis of Differential Privacy's Generalization Guarantees.
Proceedings of the 11th Innovations in Theoretical Computer Science Conference, 2020

Oracle Efficient Private Non-Convex Optimization.
Proceedings of the 37th International Conference on Machine Learning, 2020

Optimal, truthful, and private securities lending.
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.
J. Priv. Confidentiality, 2019

Differentially Private Objective Perturbation: Beyond Smoothness and Convexity.
CoRR, 2019

Eliciting and Enforcing Subjective Individual Fairness.
CoRR, 2019

How to Use Heuristics for Differential Privacy.
Proceedings of the 60th IEEE Annual Symposium on Foundations of Computer Science, 2019

The Role of Interactivity in Local Differential Privacy.
Proceedings of the 60th IEEE Annual Symposium on Foundations of Computer Science, 2019

An Empirical Study of Rich Subgroup Fairness for Machine Learning.
Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

Fair Algorithms for Learning in Allocation Problems.
Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

2018
Mitigating Bias in Adaptive Data Gathering via Differential Privacy.
Proceedings of the 35th International Conference on Machine Learning, 2018

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness.
Proceedings of the 35th International Conference on Machine Learning, 2018

Meritocratic Fairness for Infinite and Contextual Bandits.
Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018

2017
A Convex Framework for Fair Regression.
CoRR, 2017

2016
Rawlsian Fairness for Machine Learning.
CoRR, 2016


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