Matthew Joseph

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 34

Top Topics

Differential Privacy

Relational Reinforcement Learning

Differentially Private

Top Venues

Publications

Matthew Joseph, Alexander Yu
Some Constructions of Private, Efficient, and Optimal K-Norm and Elliptic Gaussian Noise. COLT (2024)
Travis Dick, Jennifer Gillenwater, Matthew Joseph
Better Private Linear Regression Through Better Private Feature Selection. NeurIPS (2023)
Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii
Easy Differentially Private Linear Regression. ICLR (2023)
Travis Dick, Jennifer Gillenwater, Matthew Joseph
Better Private Linear Regression Through Better Private Feature Selection. CoRR (2023)
Matthew Joseph, Alexander Yu
Some Efficient and Optimal K-Norm Mechanisms. CoRR (2023)
Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii
Easy Differentially Private Linear Regression. CoRR (2022)
Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng
Shuffle Private Stochastic Convex Optimization. ICLR (2022)
Matthew Joseph, Jieming Mao, Aaron Roth
Exponential Separations in Local Privacy. ACM Trans. Algorithms 18 (4) (2022)
Kareem Amin, Jennifer Gillenwater, Matthew Joseph, Alex Kulesza, Sergei Vassilvitskii
Plume: Differential Privacy at Scale. CoRR (2022)
Jennifer Gillenwater, Matthew Joseph, Andres Muñoz Medina, Mónica Ribero Diaz
A Joint Exponential Mechanism For Differentially Private Top-k. ICML (2022)
Jennifer Gillenwater, Matthew Joseph, Andrés Muñoz Medina, Mónica Ribero
A Joint Exponential Mechanism For Differentially Private Top-k. CoRR (2022)
Jennifer Gillenwater, Matthew Joseph, Alex Kulesza
Differentially Private Quantiles. CoRR (2021)
Jennifer Gillenwater, Matthew Joseph, Alex Kulesza
Differentially Private Quantiles. ICML (2021)
Victor Balcer, Albert Cheu, Matthew Joseph, Jieming Mao
Connecting Robust Shuffle Privacy and Pan-Privacy. SODA (2021)
Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng
Shuffle Private Stochastic Convex Optimization. CoRR (2021)
Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner
Local Differential Privacy for Evolving Data. J. Priv. Confidentiality 10 (1) (2020)
Kareem Amin, Matthew Joseph, Jieming Mao
Pan-Private Uniformity Testing. COLT (2020)
Matthew Joseph, Jieming Mao, Aaron Roth
Exponential Separations in Local Differential Privacy. SODA (2020)
Victor Balcer, Albert Cheu, Matthew Joseph, Jieming Mao
Connecting Robust Shuffle Privacy and Pan-Privacy. CoRR (2020)
Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu
Locally Private Gaussian Estimation. NeurIPS (2019)
Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth
The Role of Interactivity in Local Differential Privacy. CoRR (2019)
Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth
The Role of Interactivity in Local Differential Privacy. FOCS (2019)
Kareem Amin, Matthew Joseph, Jieming Mao
Pan-Private Uniformity Testing. CoRR (2019)
Matthew Joseph, Jieming Mao, Aaron Roth
Exponential Separations in Local Differential Privacy Through Communication Complexity. CoRR (2019)
Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner
Local Differential Privacy for Evolving Data. NeurIPS (2018)
Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner
Local Differential Privacy for Evolving Data. CoRR (2018)
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
Meritocratic Fairness for Infinite and Contextual Bandits. AIES (2018)
Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu
Locally Private Gaussian Estimation. CoRR (2018)
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
A Convex Framework for Fair Regression. CoRR (2017)
Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
Fairness in Reinforcement Learning. ICML (2017)
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
Fairness in Learning: Classic and Contextual Bandits. CoRR (2016)
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
Fairness in Learning: Classic and Contextual Bandits. NIPS (2016)
Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
Fair Learning in Markovian Environments. CoRR (2016)
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
Rawlsian Fairness for Machine Learning. CoRR (2016)