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Seth Neel
Seth Neel
Asst Professor, Harvard University
Geverifieerd e-mailadres voor hbs.edu - Homepage
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Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
M Kearns, S Neel, A Roth, ZS Wu
International Conference on Machine Learning (ICML 18), 2018
8472018
A convex framework for fair regression
R Berk, H Heidari, S Jabbari, M Joseph, M Kearns, J Morgenstern, S Neel, ...
Fairness, Accountability, and Transparency in Machine Learning (FATML 17), 2017
3672017
An empirical study of rich subgroup fairness for machine learning
M Kearns, S Neel, A Roth, ZS Wu
Conference on Fairness, Accountability, and Transparency (FAT* 19), 2019
2212019
Descent-to-delete: Gradient-based methods for machine unlearning
S Neel, A Roth, S Sharifi-Malvajerdi
The 32nd International Conference on Algorithmic Learning Theory, 2021
1892021
Fair algorithms for infinite and contextual bandits
M Joseph, M Kearns, J Morgenstern, S Neel, A Roth
AAAI/AIES 18, 2018
177*2018
Adaptive Machine Unlearning
V Gupta, C Jung, S Neel, A Roth, S Sharifi-Malvajerdi, C Waites
NEURIPS 2021, 2021
1282021
Accuracy first: Selecting a differential privacy level for accuracy constrained erm
K Ligett, S Neel, A Roth, B Waggoner, SZ Wu
Advances in Neural Information Processing Systems (NEURIPS 17), 2017
1022017
Eliciting and enforcing subjective individual fairness
C Jung, M Kearns, S Neel, A Roth, L Stapleton, ZS Wu
Symposium on Foundations of Responsible Computing (FORC), 2021
93*2021
Fair algorithms for learning in allocation problems
H Elzayn, S Jabbari, C Jung, M Kearns, S Neel, A Roth, Z Schutzman
Conference on Fairness, Accountability, and Transparency (FAT* 19), 2019
902019
The Role of Interactivity in Local Differential Privacy
M Joseph, J Mao, S Neel, A Roth
Foundations of Computer Science (FOCS 19), 2019
882019
A New Analysis of Differential Privacy's Generalization Guarantees
C Jung, K Ligett, S Neel, A Roth, S Sharifi-Malvajerdi, M Shenfeld
Innovations in Theoretical Computer Science (ITCS), Spotlight, 2020
442020
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
S Neel, A Roth
International Conference on Machine Learning (ICML 18), 2018
392018
In-context unlearning: Language models as few shot unlearners
M Pawelczyk, S Neel, H Lakkaraju
ICML 2024, 2023
342023
How to Use Heuristics for Differential Privacy
S Neel, A Roth, SZ Wu
Foundations of Computer Science (FOCS 19), 2018
222018
Aztec castles and the dP3 quiver
M Leoni, G Musiker, S Neel, P Turner
Journal of Physics A: Mathematical and Theoretical 47 (47), 474011, 2014
212014
Privacy issues in large language models: A survey
S Neel, P Chang
arXiv preprint arXiv:2312.06717, 2023
202023
On the Privacy Risks of Algorithmic Recourse
M Pawelczyk, H Lakkaraju, S Neel
AI STATS 2023, 2023
192023
Oracle efficient private non-convex optimization
S Neel, A Roth, G Vietri, S Wu
International conference on machine learning, 7243-7252, 2020
19*2020
MoPe: Model Perturbation-based Privacy Attacks on Language Models
M Li, J Wang, J Wang, S Neel
EMNLP '23, NEURIPS '23 Workshop on Socially Responsible Language Models, 2023
62023
Pandora's White-Box: Increased Training Data Leakage in Open LLMs
JG Wang, J Wang, M Li, S Neel
arXiv preprint arXiv:2402.17012, 2024
42024
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