Seth Neel

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Aaron RothProfessor of Computer Science, University of PennsylvaniaAdresse e-mail validée de cis.upenn.edu
Michael KearnsProfessor of Computer Science, University of PennsylvaniaAdresse e-mail validée de cis.upenn.edu
Zhiwei Steven WuCarnegie Mellon UniversityAdresse e-mail validée de andrew.cmu.edu
Matthew JosephGoogleAdresse e-mail validée de google.com
Jamie MorgensternUniversity of WashingtonAdresse e-mail validée de cs.washington.edu
Christopher JungStanford UniversityAdresse e-mail validée de stanford.edu
Saeed Sharifi-MalvajerdiResearch Assistant Professor, Toyota Technological Institute at Chicago (TTIC)Adresse e-mail validée de ttic.edu
Shahin JabbariDrexel UniversityAdresse e-mail validée de drexel.edu
Hoda HeidariCarnegie Mellon UniversityAdresse e-mail validée de cmu.edu
Katrina LigettHebrew UniversityAdresse e-mail validée de mail.huji.ac.il
Logan StapletonUniversity of MinnesotaAdresse e-mail validée de umn.edu
Chris WaitesStanford UniversityAdresse e-mail validée de stanford.edu
VARUN GUPTAIMTS INSTITUTEAdresse e-mail validée de imtsinstitute.com
Bo WaggonerUniversity of Colorado, BoulderAdresse e-mail validée de colorado.edu
Martin PawelczykPostdoc, Harvard UniversityAdresse e-mail validée de uni-tuebingen.de
Hadi ElzaynStanford UniversityAdresse e-mail validée de stanford.edu
Zachary SchutzmanMITAdresse e-mail validée de schutzman.net
Jieming MaoGoogle Research NYCAdresse e-mail validée de google.com
moshe shenfeldPhD student CS, the Hebrew universityAdresse e-mail validée de mail.huji.ac.il

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Seth Neel

Asst Professor, Harvard University

Adresse e-mail validée de hbs.edu - Page d'accueil

computer science machine learning privacy fairness


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	814	2018
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	353	2017
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	208	2019
Fair algorithms for infinite and contextual bandits M Joseph, M Kearns, J Morgenstern, S Neel, A Roth AAAI/AIES 18, 2018	173*	2018
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	172	2021
Adaptive Machine Unlearning V Gupta, C Jung, S Neel, A Roth, S Sharifi-Malvajerdi, C Waites NEURIPS 2021, 2021	116	2021
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	101	2017
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	89*	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	86	2019
The Role of Interactivity in Local Differential Privacy M Joseph, J Mao, S Neel, A Roth Foundations of Computer Science (FOCS 19), 2019	85	2019
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	42	2020
Mitigating Bias in Adaptive Data Gathering via Differential Privacy S Neel, A Roth International Conference on Machine Learning (ICML 18), 2018	38	2018
In-context unlearning: Language models as few shot unlearners M Pawelczyk, S Neel, H Lakkaraju arXiv preprint arXiv:2310.07579, 2023	23	2023
How to Use Heuristics for Differential Privacy S Neel, A Roth, SZ Wu Foundations of Computer Science (FOCS 19), 2018	22	2018
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	21	2014
Oracle efficient private non-convex optimization S Neel, A Roth, G Vietri, S Wu International conference on machine learning, 7243-7252, 2020	19*	2020
On the Privacy Risks of Algorithmic Recourse M Pawelczyk, H Lakkaraju, S Neel AI STATS 2023, 2023	15	2023
Privacy issues in large language models: A survey S Neel, P Chang arXiv preprint arXiv:2312.06717, 2023	10	2023
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	5	2023
Optimal, Truthful, and Private Securities Lending E Diana, M Kearns, S Neel, A Roth ACM Conference on AI in Finance CAIF20, NEURIPS19 Workshop on Robust AI in …, 2020	2	2020

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