J
Jieming Mao
Researcher at Google
Publications - 71
Citations - 932
Jieming Mao is an academic researcher from Google. The author has contributed to research in topics: Upper and lower bounds & Differential privacy. The author has an hindex of 17, co-authored 63 publications receiving 702 citations. Previous affiliations of Jieming Mao include University of Pennsylvania & Princeton University.
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The Role of Interactivity in Local Differential Privacy
TL;DR: The power of interactivity in local differential privacy is studied, and it is shown that for a large class of compound hypothesis testing problems, a simple noninteractive test is optimal among the class of all (possibly fully interactive) tests.
Posted Content
Parallel Algorithms for Select and Partition with Noisy Comparisons
TL;DR: This work evaluates algorithms based both on their total runtime and the number of interactive rounds in three comparison models: noiseless, erasure, and noisy (where comparisons are correct with probability 1/2 + γ/2 and incorrect otherwise).
Proceedings Article
Differentially Private Fair Learning
Matthew Jagielski,Michael Kearns,Jieming Mao,Alina Oprea,Aaron Roth,Saeed Sharifi-Malvajerdi,Jonathan Ullman +6 more
TL;DR: In this article, the authors design two learning algorithms that simultaneously promise differential privacy and equalized odds, a "fairness" condition that corresponds to equalizing false positive and negative rates across protected groups.
Proceedings ArticleDOI
The Role of Interactivity in Local Differential Privacy
TL;DR: In this article, the power of interactivity in local differential privacy was studied, and it was shown that for a large class of compound hypothesis testing problems, a simple noninteractive test is optimal among the class of all (possibly fully interactive) tests.
Proceedings Article
A nearly instance optimal algorithm for top-k ranking under the multinomial logit model
Xi Chen,Yuanzhi Li,Jieming Mao +2 more
TL;DR: In this article, the authors study the active learning problem of top-k ranking from multi-wise comparisons under the popular multinomial logit model and propose a new active ranking algorithm without using any information about the underlying items' preference scores.