Workshop paper
Proceedings of the NeurIPS Workshop on Privacy Preserving Machine Learning (PPML), 2018
Assistant Professor of Stats & Data Science at UChicago
APA
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Schofield, A., Schein, A., Wu, Z. S., & Wallach, H. (2018). A Variational Inference Approach for Locally Private Inference of Poisson Factorization Models. In Proceedings of the NeurIPS Workshop on Privacy Preserving Machine Learning (PPML).
Chicago/Turabian
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Schofield, Alexandra, Aaron Schein, Zhiwei Steven Wu, and Hanna Wallach. “A Variational Inference Approach for Locally Private Inference of Poisson Factorization Models.” In Proceedings of the NeurIPS Workshop on Privacy Preserving Machine Learning (PPML), 2018.
MLA
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Schofield, Alexandra, et al. “A Variational Inference Approach for Locally Private Inference of Poisson Factorization Models.” Proceedings of the NeurIPS Workshop on Privacy Preserving Machine Learning (PPML), 2018.
BibTeX Click to copy
@inproceedings{alexandra2018a,
title = {A Variational Inference Approach for Locally Private Inference of Poisson Factorization Models},
year = {2018},
author = {Schofield, Alexandra and Schein, Aaron and Wu, Zhiwei Steven and Wallach, Hanna},
booktitle = {Proceedings of the NeurIPS Workshop on Privacy Preserving Machine Learning (PPML)}
}