Seth Neel

Cited by

	All	Since 2019
Citations	2790	2669
h-index	18	17
i10-index	19	18

680

340

170

510

2016201720182019202020212022202320247 28 76 186 324 377 521 576 679

Public access

View all

6 articles

0 articles

available

not available

Based on funding mandates

Co-authors

Aaron RothProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Michael KearnsProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Zhiwei Steven WuCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Matthew JosephGoogleVerified email at google.com
Jamie MorgensternUniversity of WashingtonVerified email at cs.washington.edu
Christopher JungStanford UniversityVerified email at stanford.edu
Saeed Sharifi-MalvajerdiResearch Assistant Professor, Toyota Technological Institute at Chicago (TTIC)Verified email at ttic.edu
Shahin JabbariDrexel UniversityVerified email at drexel.edu
Hoda HeidariCarnegie Mellon UniversityVerified email at cmu.edu
Martin PawelczykPostdoc, Harvard UniversityVerified email at uni-tuebingen.de
Katrina LigettHebrew UniversityVerified email at mail.huji.ac.il
Logan StapletonAssistant Professor, Vassar College; PhD Candidate, University of MinnesotaVerified email at vassar.edu
Chris WaitesStanford UniversityVerified email at stanford.edu
VARUN GUPTAIMTS INSTITUTEVerified email at imtsinstitute.com
Bo WaggonerUniversity of Colorado, BoulderVerified email at colorado.edu
Hadi ElzaynStanford UniversityVerified email at stanford.edu
Zachary SchutzmanMITVerified email at schutzman.net
Jieming MaoGoogle Research NYCVerified email at google.com
moshe shenfeldPhD student CS, the Hebrew universityVerified email at mail.huji.ac.il

Seth Neel

Asst Professor, Harvard University

Verified email at hbs.edu - Homepage

computer science machine learning privacy fairness


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	910	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	387	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	231	2019
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	230	2021
Fair algorithms for infinite and contextual bandits M Joseph, M Kearns, J Morgenstern, S Neel, A Roth AAAI/AIES 18, 2018	191*	2018
Adaptive Machine Unlearning V Gupta, C Jung, S Neel, A Roth, S Sharifi-Malvajerdi, C Waites NEURIPS 2021, 2021	156	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	108	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	99*	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	95	2019
The Role of Interactivity in Local Differential Privacy M Joseph, J Mao, S Neel, A Roth Foundations of Computer Science (FOCS 19), 2019	90	2019
In-context unlearning: Language models as few shot unlearners M Pawelczyk, S Neel, H Lakkaraju ICML 2024, 2024	62	2024
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	49	2020
Mitigating Bias in Adaptive Data Gathering via Differential Privacy S Neel, A Roth International Conference on Machine Learning (ICML 18), 2018	41	2018
Privacy issues in large language models: A survey S Neel, P Chang arXiv preprint arXiv:2312.06717, 2023	33	2023
On the Privacy Risks of Algorithmic Recourse M Pawelczyk, H Lakkaraju, S Neel AI STATS 2023, 2023	23	2023
How to Use Heuristics for Differential Privacy S Neel, A Roth, SZ Wu Foundations of Computer Science (FOCS 19), 2018	23	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	20*	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	11	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	4	2020

The system can't perform the operation now. Try again later.

Articles 1–20

Citations per year

Duplicate citations

Merged citations

Add co-authorsCo-authors

Follow

Cited by

Co-authors