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Seth Neel
Person information
- affiliation: Harvard University, USA
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2020 – today
- 2024
- [i23]Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel:
Machine Unlearning Fails to Remove Data Poisoning Attacks. CoRR abs/2406.17216 (2024) - 2023
- [c17]Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel:
On the Privacy Risks of Algorithmic Recourse. AISTATS 2023: 9680-9696 - [c16]Marvin Li, Jason Wang, Jeffrey G. Wang, Seth Neel:
MoPe: Model Perturbation based Privacy Attacks on Language Models. EMNLP 2023: 13647-13660 - [i22]Peter W. Chang, Leor Fishman, Seth Neel:
Model Explanation Disparities as a Fairness Diagnostic. CoRR abs/2303.01704 (2023) - [i21]Seth Neel:
PRIMO: Private Regression in Multiple Outcomes. CoRR abs/2303.04195 (2023) - [i20]Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju:
In-Context Unlearning: Language Models as Few Shot Unlearners. CoRR abs/2310.07579 (2023) - [i19]Lukman Olagoke, Salil Vadhan, Seth Neel:
Black-Box Training Data Identification in GANs via Detector Networks. CoRR abs/2310.12063 (2023) - [i18]Marvin Li, Jason Wang, Jeffrey G. Wang, Seth Neel:
MoPe: Model Perturbation-based Privacy Attacks on Language Models. CoRR abs/2310.14369 (2023) - [i17]Seth Neel, Peter W. Chang:
Privacy Issues in Large Language Models: A Survey. CoRR abs/2312.06717 (2023) - 2022
- [i16]Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel:
On the Privacy Risks of Algorithmic Recourse. CoRR abs/2211.05427 (2022) - 2021
- [c15]Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi:
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. ALT 2021: 931-962 - [c14]Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth
, Logan Stapleton, Zhiwei Steven Wu
:
An Algorithmic Framework for Fairness Elicitation. FORC 2021: 2:1-2:19 - [c13]Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites:
Adaptive Machine Unlearning. NeurIPS 2021: 16319-16330 - [c12]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: 9 - [i15]Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites:
Adaptive Machine Unlearning. CoRR abs/2106.04378 (2021) - 2020
- [c11]Emily Diana, Michael Kearns, Seth Neel, Aaron Roth
:
Optimal, truthful, and private securities lending. ICAIF 2020: 48:1-48:8 - [c10]Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu:
Oracle Efficient Private Non-Convex Optimization. ICML 2020: 7243-7252 - [c9]Christopher Jung, Katrina Ligett
, Seth Neel, Aaron Roth
, Saeed Sharifi-Malvajerdi, Moshe Shenfeld:
A New Analysis of Differential Privacy's Generalization Guarantees. ITCS 2020: 31:1-31:17 - [i14]Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi:
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. CoRR abs/2007.02923 (2020)
2010 – 2019
- 2019
- [j1]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) - [c8]Michael J. Kearns, Seth Neel, Aaron Roth
, Zhiwei Steven Wu
:
An Empirical Study of Rich Subgroup Fairness for Machine Learning. FAT 2019: 100-109 - [c7]Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth
, Zachary Schutzman
:
Fair Algorithms for Learning in Allocation Problems. FAT 2019: 170-179 - [c6]Seth Neel, Aaron Roth
, Zhiwei Steven Wu:
How to Use Heuristics for Differential Privacy. FOCS 2019: 72-93 - [c5]Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth
:
The Role of Interactivity in Local Differential Privacy. FOCS 2019: 94-105 - [i13]Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth:
The Role of Interactivity in Local Differential Privacy. CoRR abs/1904.03564 (2019) - [i12]Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu:
Eliciting and Enforcing Subjective Individual Fairness. CoRR abs/1905.10660 (2019) - [i11]Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu:
Differentially Private Objective Perturbation: Beyond Smoothness and Convexity. CoRR abs/1909.01783 (2019) - [i10]Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld:
A New Analysis of Differential Privacy's Generalization Guarantees. CoRR abs/1909.03577 (2019) - [i9]Emily Diana, Michael J. Kearns, Seth Neel, Aaron Roth:
Optimal, Truthful, and Private Securities Lending. CoRR abs/1912.06202 (2019) - 2018
- [c4]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
:
Meritocratic Fairness for Infinite and Contextual Bandits. AIES 2018: 158-163 - [c3]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. ICML 2018: 2569-2577 - [c2]Seth Neel, Aaron Roth:
Mitigating Bias in Adaptive Data Gathering via Differential Privacy. ICML 2018: 3717-3726 - [i8]Seth Neel, Aaron Roth:
Mitigating Bias in Adaptive Data Gathering via Differential Privacy. CoRR abs/1806.02329 (2018) - [i7]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
An Empirical Study of Rich Subgroup Fairness for Machine Learning. CoRR abs/1808.08166 (2018) - [i6]Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman
:
Fair Algorithms for Learning in Allocation Problems. CoRR abs/1808.10549 (2018) - [i5]Seth Neel, Aaron Roth, Zhiwei Steven Wu:
How to Use Heuristics for Differential Privacy. CoRR abs/1811.07765 (2018) - 2017
- [c1]Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM. NIPS 2017: 2566-2576 - [i4]Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM. CoRR abs/1705.10829 (2017) - [i3]Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
A Convex Framework for Fair Regression. CoRR abs/1706.02409 (2017) - [i2]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. CoRR abs/1711.05144 (2017) - 2016
- [i1]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
Rawlsian Fairness for Machine Learning. CoRR abs/1610.09559 (2016)
Coauthor Index
aka: Michael J. Kearns
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last updated on 2024-07-27 16:42 CEST by the dblp team
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