Matthew Joseph

According to our database1, Matthew Joseph authored at least 17 papers between 2016 and 2022.

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Bibliography

2022
Plume: Differential Privacy at Scale.
CoRR, 2022

A Joint Exponential Mechanism For Differentially Private Top-k.
Proceedings of the International Conference on Machine Learning, 2022

2021
Shuffle Private Stochastic Convex Optimization.
CoRR, 2021

Connecting Robust Shuffle Privacy and Pan-Privacy.
Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms, 2021

Differentially Private Quantiles.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Local Differential Privacy for Evolving Data.
J. Priv. Confidentiality, 2020

Exponential Separations in Local Differential Privacy.
Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, 2020

Pan-Private Uniformity Testing.
Proceedings of the Conference on Learning Theory, 2020

2019
Exponential Separations in Local Differential Privacy Through Communication Complexity.
CoRR, 2019

Locally Private Gaussian Estimation.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

The Role of Interactivity in Local Differential Privacy.
Proceedings of the 60th IEEE Annual Symposium on Foundations of Computer Science, 2019

2018
Meritocratic Fairness for Infinite and Contextual Bandits.
Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018

2017
A Convex Framework for Fair Regression.
CoRR, 2017

Fairness in Reinforcement Learning.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Rawlsian Fairness for Machine Learning.
CoRR, 2016

Fair Learning in Markovian Environments.
CoRR, 2016

Fairness in Learning: Classic and Contextual Bandits.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016


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