Joseph, Matthew

14 publications

ICML 2025 Approximate Differential Privacy of the $\ell_2$ Mechanism Matthew Joseph, Alex Kulesza, Alexander Yu

ICLR 2025 Privately Counting Partially Ordered Data Matthew Joseph, Mónica Ribero, Alexander Yu

COLT 2024 Some Constructions of Private, Efficient, and Optimal $k$-Norm and Elliptic Gaussian Noise Matthew Joseph, Alexander Yu

NeurIPS 2023 Better Private Linear Regression Through Better Private Feature Selection Travis Dick, Jennifer Gillenwater, Matthew Joseph

ICLR 2023 Easy Differentially Private Linear Regression Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii

ICML 2022 A Joint Exponential Mechanism for Differentially Private Top-$k$ Jennifer Gillenwater, Matthew Joseph, Andres Munoz, Monica Ribero Diaz

ICLR 2022 Shuffle Private Stochastic Convex Optimization Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng

NeurIPSW 2021 A Joint Exponential Mechanism for Differentially Private Top-K Set Andres Munoz Medina, Matthew Joseph, Jennifer Gillenwater, Mónica Ribero

ICML 2021 Differentially Private Quantiles Jennifer Gillenwater, Matthew Joseph, Alex Kulesza

COLT 2020 Pan-Private Uniformity Testing Kareem Amin, Matthew Joseph, Jieming Mao

NeurIPS 2019 Locally Private Gaussian Estimation Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Steven Z. Wu

NeurIPS 2018 Local Differential Privacy for Evolving Data Matthew Joseph, Aaron Roth, Jonathan Ullman, Bo Waggoner

ICML 2017 Fairness in Reinforcement Learning Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth

NeurIPS 2016 Fairness in Learning: Classic and Contextual Bandits Matthew Joseph, Michael Kearns, Jamie H Morgenstern, Aaron Roth