Fairness in Learning: Classic and Contextual Bandits. - Matthew Joseph; Michael J. Kearns; Jamie Morgenstern; Aaron Roth 001 (NIPS, 2016)

Fair Learning in Markovian Environments. - Shahin Jabbari; Matthew Joseph; Michael J. Kearns; Jamie Morgenstern; Aaron Roth 001 (CoRR, 2016)

Rawlsian Fairness for Machine Learning. - Matthew Joseph; Michael J. Kearns; Jamie Morgenstern; Seth Neel; Aaron Roth 001 (CoRR, 2016)

Fairness in Reinforcement Learning. - Shahin Jabbari; Matthew Joseph; Michael J. Kearns; Jamie Morgenstern; Aaron Roth 001 (ICML, 2017)

A Convex Framework for Fair Regression. - Richard Berk; Hoda Heidari; Shahin Jabbari; Matthew Joseph; Michael J. Kearns; Jamie Morgenstern; Seth Neel; Aaron Roth 001 (CoRR, 2017)

Meritocratic Fairness for Infinite and Contextual Bandits. - Matthew Joseph; Michael J. Kearns; Jamie Morgenstern; Seth Neel; Aaron Roth 001 (AIES, 2018)

The Role of Interactivity in Local Differential Privacy. - Matthew Joseph; Jieming Mao; Seth Neel; Aaron Roth 001 (FOCS, 2019)

Locally Private Gaussian Estimation. - Matthew Joseph; Janardhan Kulkarni; Jieming Mao; Zhiwei Steven Wu (NeurIPS, 2019)

Exponential Separations in Local Differential Privacy Through Communication Complexity. - Matthew Joseph; Jieming Mao; Aaron Roth 001 (CoRR, 2019)

Pan-Private Uniformity Testing. - Kareem Amin 002; Matthew Joseph; Jieming Mao (COLT, 2020)

Exponential Separations in Local Differential Privacy. - Matthew Joseph; Jieming Mao; Aaron Roth 001 (SODA, 2020)

Local Differential Privacy for Evolving Data. - Matthew Joseph; Aaron Roth 001; Jonathan R. Ullman; Bo Waggoner (J. Priv. Confidentiality, 2020)

Differentially Private Quantiles. - Jennifer Gillenwater; Matthew Joseph; Alex Kulesza (ICML, 2021)

Connecting Robust Shuffle Privacy and Pan-Privacy. - Victor Balcer; Albert Cheu; Matthew Joseph; Jieming Mao (SODA, 2021)

Shuffle Private Stochastic Convex Optimization. - Albert Cheu; Matthew Joseph; Jieming Mao; Binghui Peng (ICLR, 2022)

A Joint Exponential Mechanism For Differentially Private Top-k. - Jennifer Gillenwater; Matthew Joseph; Andres Muñoz Medina; Mónica Ribero Diaz (ICML, 2022)

Plume: Differential Privacy at Scale. - Kareem Amin 002; Jennifer Gillenwater; Matthew Joseph; Alex Kulesza; Sergei Vassilvitskii (CoRR, 2022)

Exponential Separations in Local Privacy. - Matthew Joseph; Jieming Mao; Aaron Roth 001 (ACM Trans. Algorithms, 2022)

Easy Differentially Private Linear Regression. - Kareem Amin 002; Matthew Joseph; Mónica Ribero; Sergei Vassilvitskii (ICLR, 2023)

Better Private Linear Regression Through Better Private Feature Selection. - Travis Dick; Jennifer Gillenwater; Matthew Joseph (NeurIPS, 2023)

Some Efficient and Optimal K-Norm Mechanisms. - Matthew Joseph; Alexander Yu (CoRR, 2023)

Some Constructions of Private, Efficient, and Optimal K-Norm and Elliptic Gaussian Noise. - Matthew Joseph; Alexander Yu (COLT, 2024)

Privately Counting Partially Ordered Data. - Matthew Joseph; Mónica Ribero; Alexander Yu (ICLR, 2025)

Approximate Differential Privacy of the ℓ2 Mechanism. - Matthew Joseph; Alex Kulesza; Alexander Yu (CoRR, 2025)