Online and Differentially-Private Tensor Decomposition
By Yining Wang, Anima Anandkumar. 2016
39 citations recorded by the source corpus.
In this paper, we resolve many of the key algorithmic questions regarding robustness, memory efficiency, and differential privacy of tensor decomposition. We propose simple variants of the tensor power method which enjoy these strong properties. We present the first guarantees for online tensor power method which has a linear memory requirement. Moreover, we present a noise calibrated tensor power method with efficient privacy guarantees. At the heart of all these guarantees lies a careful perturbation analysis derived in this paper which improves up on the existing results significantly.
Subfields: Computational Mathematics
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In Computational Mathematics
- Xiaoqian Jiang
- Inderjit S. Dhillon
- Zhao Song
- Kai Zhong
- Joyce Ho
- Peter L. Bartlett
- Prateek Jain
- Samet Oymak
Citations in the corpus
- Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis (2019, background): “[27] is the first proposed differentially private tensor decomposition work.”
- Recovery Guarantees for One-hidden-layer Neural Networks (2017, methodology): “However, by making some assumptions, tensor decomposition method becomes efficient (Anandkumar et al., 2014; Wang et al., 2015; Wang & Anandkumar, 2016; Song et al., 2016).”