Matthew Joseph

39 papers A* 15C 1Journal 22Unranked 1
YearRankTypeTitle / Venue / Authors
2026 J jnl
Missing Mass for Differentially Private Domain Discovery.
CoRR
Travis Dick, Matthew Joseph, Vinod Raman
2025 J jnl
Approximate Differential Privacy of the ℓ
CoRR
Matthew Joseph, Alex Kulesza, Alexander Yu
2025 A* conf
Approximate Differential Privacy of the ℓ2 Mechanism.
ICML
Matthew Joseph, Alex Kulesza, Alexander Yu
2025 A* conf
Privately Counting Partially Ordered Data.
ICLR
Matthew Joseph, Mónica Ribero, Alexander Yu
2024 J jnl
Privately Counting Partially Ordered Data.
CoRR
Matthew Joseph, Mónica Ribero, Alexander Yu
2024 A* conf
Some Constructions of Private, Efficient, and Optimal K-Norm and Elliptic Gaussian Noise.
COLT
Matthew Joseph, Alexander Yu
2023 A* conf
Better Private Linear Regression Through Better Private Feature Selection.
NeurIPS
Travis Dick, Jennifer Gillenwater, Matthew Joseph
2023 J jnl
Better Private Linear Regression Through Better Private Feature Selection.
CoRR
Travis Dick, Jennifer Gillenwater, Matthew Joseph
2023 A* conf
Easy Differentially Private Linear Regression.
ICLR
Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii
2023 J jnl
Some Efficient and Optimal K-Norm Mechanisms.
CoRR
Matthew Joseph, Alexander Yu
2022 A* conf
A Joint Exponential Mechanism For Differentially Private Top-k.
ICML
Jennifer Gillenwater, Matthew Joseph, Andres Muñoz Medina, Mónica Ribero Diaz
2022 J jnl
A Joint Exponential Mechanism For Differentially Private Top-k.
CoRR
Jennifer Gillenwater, Matthew Joseph, Andrés Muñoz Medina, Mónica Ribero
2022 J jnl
Easy Differentially Private Linear Regression.
CoRR
Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii
2022 J jnl
Exponential Separations in Local Privacy.
ACM Trans. Algorithms
Matthew Joseph, Jieming Mao, Aaron Roth
2022 J jnl
Plume: Differential Privacy at Scale.
CoRR
Kareem Amin, Jennifer Gillenwater, Matthew Joseph, Alex Kulesza, Sergei Vassilvitskii
2022 A* conf
Shuffle Private Stochastic Convex Optimization.
ICLR
Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng
2021 A* conf
Connecting Robust Shuffle Privacy and Pan-Privacy.
SODA
Victor Balcer, Albert Cheu, Matthew Joseph, Jieming Mao
2021 A* conf
Differentially Private Quantiles.
ICML
Jennifer Gillenwater, Matthew Joseph, Alex Kulesza
2021 J jnl
Differentially Private Quantiles.
CoRR
Jennifer Gillenwater, Matthew Joseph, Alex Kulesza
2021 J jnl
Shuffle Private Stochastic Convex Optimization.
CoRR
Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng
2020 J jnl
Connecting Robust Shuffle Privacy and Pan-Privacy.
CoRR
Victor Balcer, Albert Cheu, Matthew Joseph, Jieming Mao
2020 A* conf
Exponential Separations in Local Differential Privacy.
SODA
Matthew Joseph, Jieming Mao, Aaron Roth
2020 J jnl
Local Differential Privacy for Evolving Data.
J. Priv. Confidentiality
Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner
2020 A* conf
Pan-Private Uniformity Testing.
COLT
Kareem Amin, Matthew Joseph, Jieming Mao
2019 J jnl
Exponential Separations in Local Differential Privacy Through Communication Complexity.
CoRR
Matthew Joseph, Jieming Mao, Aaron Roth
2019 A* conf
Locally Private Gaussian Estimation.
NeurIPS
Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu
2019 J jnl
Pan-Private Uniformity Testing.
CoRR
Kareem Amin, Matthew Joseph, Jieming Mao
2019 A* conf
The Role of Interactivity in Local Differential Privacy.
FOCS
Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth
2019 J jnl
The Role of Interactivity in Local Differential Privacy.
CoRR
Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth
2018 A* conf
Local Differential Privacy for Evolving Data.
NeurIPS
Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner
2018 J jnl
Local Differential Privacy for Evolving Data.
CoRR
Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner
2018 J jnl
Locally Private Gaussian Estimation.
CoRR
Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu
2018 C conf
Meritocratic Fairness for Infinite and Contextual Bandits.
AIES
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
2017 J jnl
A Convex Framework for Fair Regression.
CoRR
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
2017 A* conf
Fairness in Reinforcement Learning.
ICML
Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
2016 J jnl
Fair Learning in Markovian Environments.
CoRR
Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
2016 — conf
Fairness in Learning: Classic and Contextual Bandits.
NIPS
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
2016 J jnl
Fairness in Learning: Classic and Contextual Bandits.
CoRR
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
2016 J jnl
Rawlsian Fairness for Machine Learning.
CoRR
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth