Matthew Joseph
Matthew Joseph
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Title
Cited by
Cited by
Year
Fairness in learning: Classic and contextual bandits
M Joseph, M Kearns, JH Morgenstern, A Roth
Advances in Neural Information Processing Systems, 325-333, 2016
1852016
A Convex Framework for Fair Regression
R Berk, H Heidari, S Jabbari, M Joseph, M Kearns, J Morgenstern, S Neel, ...
arXiv preprint arXiv:1706.02409, 2017
752017
Meritocratic Fairness for Infinite and Contextual Bandits
M Joseph, M Kearns, J Morgenstern, S Neel, A Roth
Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 158-163, 2018
68*2018
Fairness in Reinforcement Learning
S Jabbari, M Joseph, M Kearns, J Morgenstern, A Roth
International Conference on Machine Learning, 1617-1626, 2017
65*2017
Local differential privacy for evolving data
M Joseph, A Roth, J Ullman, B Waggoner
Advances in Neural Information Processing Systems, 2375-2384, 2018
212018
The role of interactivity in local differential privacy
M Joseph, J Mao, S Neel, A Roth
2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS …, 2019
202019
Locally private gaussian estimation
M Joseph, J Kulkarni, J Mao, SZ Wu
Advances in Neural Information Processing Systems, 2984-2993, 2019
142019
Exponential separations in local differential privacy
M Joseph, J Mao, A Roth
Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete …, 2020
112020
Pan-Private Uniformity Testing
K Amin, M Joseph, J Mao
arXiv preprint arXiv:1911.01452, 2019
42019
Connecting Robust Shuffle Privacy and Pan-Privacy
V Balcer, A Cheu, M Joseph, J Mao
arXiv preprint arXiv:2004.09481, 2020
22020
The Power of Interaction in Local Differential Privacy
M Joseph
2020
Differential Privacy Beyond the Central Model
M Joseph
University of Pennsylvania, 2020
2020
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Articles 1–12