Stebėti
Matthew Joseph
Matthew Joseph
Google
Patvirtintas el. paštas google.com - Pagrindinis puslapis
Pavadinimas
Cituota
Cituota
Metai
Fairness in learning: Classic and contextual bandits
M Joseph, M Kearns, JH Morgenstern, A Roth
Advances in Neural Information Processing Systems, 325-333, 2016
5632016
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
4202017
Fairness in Reinforcement Learning
S Jabbari, M Joseph, M Kearns, J Morgenstern, A Roth
International Conference on Machine Learning, 1617-1626, 2017
258*2017
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
193*2018
Local differential privacy for evolving data
M Joseph, A Roth, J Ullman, B Waggoner
Advances in Neural Information Processing Systems 31, 2375-2384, 2018
1092018
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
972019
Differentially private quantiles
J Gillenwater, M Joseph, A Kulesza
International Conference on Machine Learning, 3713-3722, 2021
532021
Connecting robust shuffle privacy and pan-privacy
V Balcer, A Cheu, M Joseph, J Mao
Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA …, 2021
502021
Locally private gaussian estimation
M Joseph, J Kulkarni, J Mao, SZ Wu
Advances in Neural Information Processing Systems, 2984-2993, 2019
492019
Pan-private uniformity testing
K Amin, M Joseph, J Mao
Conference on Learning Theory, 183-218, 2020
382020
Exponential separations in local differential privacy
M Joseph, J Mao, A Roth
Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete …, 2020
362020
Shuffle Private Stochastic Convex Optimization
A Cheu, M Joseph, J Mao, B Peng
International Conference on Learning Representations, 2022
302022
Plume: Differential Privacy at Scale
K Amin, J Gillenwater, M Joseph, A Kulesza, S Vassilvitskii
arXiv preprint arXiv:2201.11603, 2022
202022
Easy Differentially Private Linear Regression
K Amin, M Joseph, M Ribero, S Vassilvitskii
ICLR 2023, 2023
192023
A Joint Exponential Mechanism For Differentially Private Top-
J Gillenwater, M Joseph, A Munoz, MR Diaz
International Conference on Machine Learning, 7570-7582, 2022
132022
Better private linear regression through better private feature selection
T Dick, J Gillenwater, M Joseph
Advances in Neural Information Processing Systems 36, 53457-53474, 2023
42023
Some Constructions of Private, Efficient, and Optimal -Norm and Elliptic Gaussian Noise
M Joseph, A Yu
The Thirty Seventh Annual Conference on Learning Theory, 2723-2766, 2024
12024
Differential Privacy Beyond the Central Model
M Joseph
University of Pennsylvania, 2020
12020
Privately Counting Partially Ordered Data
M Joseph, M Ribero, A Yu
arXiv preprint arXiv:2410.06881, 2024
2024
The Power of Interaction in Local Differential Privacy
M Joseph
2020
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Straipsniai 1–20