Seth Neel
Seth Neel
Harvard Business School
Verifierad e-postadress på wharton.upenn.edu - Startsida
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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
2802018
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
1122017
Fair algorithms for infinite and contextual bandits
M Joseph, M Kearns, J Morgenstern, S Neel, A Roth
AAAI/AIES 18, 2018
97*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
632019
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
442017
Eliciting and enforcing subjective individual fairness
C Jung, M Kearns, S Neel, A Roth, L Stapleton, ZS Wu
arXiv preprint arXiv:1905.10660, 2019
312019
The Role of Interactivity in Local Differential Privacy
M Joseph, J Mao, S Neel, A Roth
Foundations of Computer Science (FOCS 19), 2019
312019
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
302019
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
192014
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
S Neel, A Roth
International Conference on Machine Learning (ICML 18), 2018
152018
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
122020
How to Use Heuristics for Differential Privacy
S Neel, A Roth, SZ Wu
Foundations of Computer Science (FOCS 19), 2018
122018
Descent-to-delete: Gradient-based methods for machine unlearning
S Neel, A Roth, S Sharifi-Malvajerdi
Algorithmic Learning Theory, 931-962, 2021
62021
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
32020
Accuracy first: Selecting a differential privacy level for accuracy-constrained ERM
K Ligett, S Neel, A Roth, B Waggoner, ZS Wu
Journal of Privacy and Confidentiality 9, 2, 2019
12019
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
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
2020
Mahalanobis Matching and Equal Percent Bias Reduction
SV Neel
Harvard College, 2015
2015
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Artiklar 1–20