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2020 – today
- 2025
[i19]Matthew Joseph, Alex Kulesza, Alexander Yu:
Approximate Differential Privacy of the ℓ2 Mechanism. CoRR abs/2502.15929 (2025)- 2024
- [c15]Matthew Joseph, Alexander Yu:
Some Constructions of Private, Efficient, and Optimal K-Norm and Elliptic Gaussian Noise. COLT 2024: 2723-2766 - [i18]Matthew Joseph, Mónica Ribero, Alexander Yu:
Privately Counting Partially Ordered Data. CoRR abs/2410.06881 (2024) - 2023
- [c14]Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii:
Easy Differentially Private Linear Regression. ICLR 2023 - [c13]Travis Dick, Jennifer Gillenwater, Matthew Joseph:
Better Private Linear Regression Through Better Private Feature Selection. NeurIPS 2023 - [i17]Travis Dick, Jennifer Gillenwater, Matthew Joseph:
Better Private Linear Regression Through Better Private Feature Selection. CoRR abs/2306.00920 (2023) - [i16]Matthew Joseph, Alexander Yu:
Some Efficient and Optimal K-Norm Mechanisms. CoRR abs/2309.15790 (2023) - 2022
- [j2]Matthew Joseph
, Jieming Mao, Aaron Roth:
Exponential Separations in Local Privacy. ACM Trans. Algorithms 18(4): 32:1-32:17 (2022) - [c12]Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng:
Shuffle Private Stochastic Convex Optimization. ICLR 2022 - [c11]Jennifer Gillenwater, Matthew Joseph, Andres Muñoz Medina, Mónica Ribero Diaz:
A Joint Exponential Mechanism For Differentially Private Top-k. ICML 2022: 7570-7582 - [i15]Kareem Amin, Jennifer Gillenwater, Matthew Joseph, Alex Kulesza, Sergei Vassilvitskii:
Plume: Differential Privacy at Scale. CoRR abs/2201.11603 (2022) - [i14]Jennifer Gillenwater, Matthew Joseph, Andrés Muñoz Medina, Mónica Ribero:
A Joint Exponential Mechanism For Differentially Private Top-k. CoRR abs/2201.12333 (2022) - [i13]Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii:
Easy Differentially Private Linear Regression. CoRR abs/2208.07353 (2022) - 2021
- [c10]Jennifer Gillenwater, Matthew Joseph, Alex Kulesza:
Differentially Private Quantiles. ICML 2021: 3713-3722 - [c9]Victor Balcer, Albert Cheu, Matthew Joseph, Jieming Mao:
Connecting Robust Shuffle Privacy and Pan-Privacy. SODA 2021: 2384-2403 - [i12]Jennifer Gillenwater, Matthew Joseph, Alex Kulesza:
Differentially Private Quantiles. CoRR abs/2102.08244 (2021) - [i11]Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng:
Shuffle Private Stochastic Convex Optimization. CoRR abs/2106.09805 (2021) - 2020
- [j1]Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner:
Local Differential Privacy for Evolving Data. J. Priv. Confidentiality 10(1) (2020) - [c8]Kareem Amin, Matthew Joseph, Jieming Mao:
Pan-Private Uniformity Testing. COLT 2020: 183-218 - [c7]Matthew Joseph, Jieming Mao, Aaron Roth:
Exponential Separations in Local Differential Privacy. SODA 2020: 515-527 - [i10]Victor Balcer, Albert Cheu, Matthew Joseph, Jieming Mao:
Connecting Robust Shuffle Privacy and Pan-Privacy. CoRR abs/2004.09481 (2020)
2010 – 2019
- 2019
- [c6]Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth:
The Role of Interactivity in Local Differential Privacy. FOCS 2019: 94-105 - [c5]Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu:
Locally Private Gaussian Estimation. NeurIPS 2019: 2980-2989 - [i9]Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth:
The Role of Interactivity in Local Differential Privacy. CoRR abs/1904.03564 (2019) - [i8]Matthew Joseph, Jieming Mao, Aaron Roth:
Exponential Separations in Local Differential Privacy Through Communication Complexity. CoRR abs/1907.00813 (2019) - [i7]Kareem Amin, Matthew Joseph, Jieming Mao:
Pan-Private Uniformity Testing. CoRR abs/1911.01452 (2019) - 2018
- [c4]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
Meritocratic Fairness for Infinite and Contextual Bandits. AIES 2018: 158-163 - [c3]Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner:
Local Differential Privacy for Evolving Data. NeurIPS 2018: 2381-2390 - [i6]Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner:
Local Differential Privacy for Evolving Data. CoRR abs/1802.07128 (2018) - [i5]Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu:
Locally Private Gaussian Estimation. CoRR abs/1811.08382 (2018) - 2017
- [c2]Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Reinforcement Learning. ICML 2017: 1617-1626 - [i4]Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
A Convex Framework for Fair Regression. CoRR abs/1706.02409 (2017) - 2016
- [c1]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Learning: Classic and Contextual Bandits. NIPS 2016: 325-333 - [i3]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Learning: Classic and Contextual Bandits. CoRR abs/1605.07139 (2016) - [i2]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
Rawlsian Fairness for Machine Learning. CoRR abs/1610.09559 (2016) - [i1]Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fair Learning in Markovian Environments. CoRR abs/1611.03071 (2016)
Coauthor Index
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