A Variational Inference Approach for Locally Private Inference of Poisson Factorization Models


Workshop paper


Alexandra Schofield, Aaron Schein, Zhiwei Steven Wu, Hanna Wallach
Proceedings of the NeurIPS Workshop on Privacy Preserving Machine Learning (PPML), 2018

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APA   Click to copy
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   Click to copy
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   Click to copy
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)}
}