Seth Neel
Seth Neel
Harvard University
Verified email at wharton.upenn.edu - Homepage
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
3462018
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
1552017
Fair algorithms for infinite and contextual bandits
M Joseph, M Kearns, J Morgenstern, S Neel, A Roth
AAAI/AIES 18, 2018
109*2018
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
782019
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
572017
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
392021
The Role of Interactivity in Local Differential Privacy
M Joseph, J Mao, S Neel, A Roth
Foundations of Computer Science (FOCS 19), 2019
382019
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
372019
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
182014
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
S Neel, A Roth
International Conference on Machine Learning (ICML 18), 2018
172018
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
162021
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
162020
How to Use Heuristics for Differential Privacy
S Neel, A Roth, SZ Wu
Foundations of Computer Science (FOCS 19), 2018
162018
Adaptive Machine Unlearning
V Gupta, C Jung, S Neel, A Roth, S Sharifi-Malvajerdi, C Waites
arXiv preprint arXiv:2106.04378, 2021
42021
Oracle efficient private non-convex optimization
S Neel, A Roth, G Vietri, S Wu
International Conference on Machine Learning, 7243-7252, 2020
42020
Differentially private objective perturbation: Beyond smoothness and convexity
S Neel, A Roth, G Vietri, ZS Wu
International Conference on Machine Learning (ICML), 2020
42020
An algorithmic framework for fairness elicitation
C Jung, M Kearns, S Neel, A Roth, L Stapleton, ZS Wu
arXiv preprint arXiv:1905.10660, 2019
22019
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
12020
Binary Quadratic Forms and the Ideal Class Group
SV Neel
Lecture Notes, Harvard University, 2012
12012
Towards Ethical Machine Learning: New Algorithms for Fairness and Privacy
SV Neel
PQDT-UK & Ireland, 2020
2020
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