How can we build algorithms that are private and/or fair?
All papers are alphabetical author order.
Connecting Robust Shuffle Privacy and Pan-Privacy. Manuscript.
With Victor Balcer, Albert Cheu, and Jieming Mao.
Pan-Private Uniformity Testing. COLT 2020.
With Kareem Amin and Jieming Mao.
Exponential Separations in Local Differential Privacy. SODA 2020. Talk.
With Jieming Mao and Aaron Roth.
Invited to TALG special issue for SODA 2020.
Locally Private Gaussian Estimation. NeurIPS 2019. Poster.
With Janardhan Kulkarni, Jieming Mao, and Zhiwei Steven Wu.
The Role of Interactivity in Local Differential Privacy. FOCS 2019. Talk.
With Jieming Mao, Seth Neel, and Aaron Roth.
Local Differential Privacy for Evolving Data. NeurIPS 2018. Spotlight Talk.
With Aaron Roth, Jonathan Ullman, and Bo Waggoner.
Meritocratic Fairness for Infinite and Contextual Bandits. AIES 2018. Poster.
With Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth.
A Convex Framework for Fair Regression. FATML 2017. Poster.
With Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, Jamie Morgenstern,
Seth Neel, and Aaron Roth.
Fairness in Reinforcement Learning. ICML 2017. Talk.
With Shahin Jabbari, Michael Kearns, Jamie Morgenstern, and Aaron Roth.
Fairness in Learning: Classic and Contextual Bandits. NIPS 2016. Poster.
With Michael Kearns, Jamie Morgenstern, and Aaron Roth.
PC: FAT* 2019; TPDP 2019, 2020.
Conference Reviews: AIES 2019; COLT 2017; EC 2018; FOCS 2019; ICLR 2020; ICML 2019, 2020; ITC 2020; NeurIPS 2016, 2018 (top reviewer!), 2019, 2020; STOC 2020.
Journal Reviews: Journal of Machine Learning Research 2020, Journal of Privacy and Confidentiality 2019.
I also enjoy cooking, falling off things, and Wikipedia's random page function.