2016/06/20 by Yining Wang, Wang, Yining, Animashree Anandkumar +1
Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Tensor decomposition and applications #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1606.06237
19 pages, 9 figures. To appear at the 30th Annual Conference on Advances in Neural Information Processing Systems (NIPS 2016), to be held at Barcelona, Spain. Fix small typos in proofs of Lemmas C.5 and C.6
openalex publication_date 2016/06/20 · arxiv created 2016/12/15 · arxiv updated 2016/12/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.