Matthew Joseph

According to our database, Matthew Joseph authored at least 21 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2023

Some Efficient and Optimal K-Norm Mechanisms.

Matthew Joseph, Alexander Yu

CoRR, 2023

Better Private Linear Regression Through Better Private Feature Selection.

Travis Dick, Jennifer Gillenwater, Matthew Joseph

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Easy Differentially Private Linear Regression.

Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Exponential Separations in Local Privacy.

Matthew Joseph, Jieming Mao, Aaron Roth

ACM Trans. Algorithms, 2022

Plume: Differential Privacy at Scale.

Kareem Amin, Jennifer Gillenwater, Matthew Joseph, Alex Kulesza, Sergei Vassilvitskii

CoRR, 2022

A Joint Exponential Mechanism For Differentially Private Top-k.

Jennifer Gillenwater, Matthew Joseph, Andres Muñoz Medina, Mónica Ribero Diaz

Proceedings of the International Conference on Machine Learning, 2022

Shuffle Private Stochastic Convex Optimization.

Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Connecting Robust Shuffle Privacy and Pan-Privacy.

Victor Balcer, Albert Cheu, Matthew Joseph, Jieming Mao

Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms, 2021

Differentially Private Quantiles.

Jennifer Gillenwater, Matthew Joseph, Alex Kulesza

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Local Differential Privacy for Evolving Data.

Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner

J. Priv. Confidentiality, 2020

Exponential Separations in Local Differential Privacy.

Matthew Joseph, Jieming Mao, Aaron Roth

Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, 2020

Pan-Private Uniformity Testing.

Kareem Amin, Matthew Joseph, Jieming Mao

Proceedings of the Conference on Learning Theory, 2020

2019

Exponential Separations in Local Differential Privacy Through Communication Complexity.

Matthew Joseph, Jieming Mao, Aaron Roth

CoRR, 2019

Locally Private Gaussian Estimation.

Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu

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.

Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth

Proceedings of the 60th IEEE Annual Symposium on Foundations of Computer Science, 2019

2018

Meritocratic Fairness for Infinite and Contextual Bandits.

Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth

Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018

2017

A Convex Framework for Fair Regression.

Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth

CoRR, 2017

Fairness in Reinforcement Learning.

Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Rawlsian Fairness for Machine Learning.

Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth

CoRR, 2016

Fair Learning in Markovian Environments.

Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth

CoRR, 2016

Fairness in Learning: Classic and Contextual Bandits.

Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016