Fetching the paper…
Reading the bibliography…
Tensor completion exhibits an interesting computational-statistical gap in terms of the number of samples needed to perform tensor estimation.
A multilinear singular value decomposition
Lieven De Lathauwer, Bart De Moor, and Joos Vandewalle. 2000 · 2000
Earlier work this paper cites.
Estimation of low-rank tensors via convex optimization
Ryota Tomioka, Kohei Hayashi, and Hisashi Kashima. 2010 · 2010
Earlier work this paper cites.
Tensor completion and low-n-rank tensor recovery via convex optimization
Silvia Gandy, Benjamin Recht, and Isao Yamada. 2011 · 2011
Earlier work this paper cites.
Statistical performance of convex tensor decomposition. In Advances in Neural Information Processing Systems . 972–980
Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, and Hisashi Kashima. 2011 · 2011
Earlier work this paper cites.
Tensor completion for estimating missing values in visual data
Ji Liu, Przemyslaw Musialski, Peter Wonka, and Jieping Ye. 2012 · 2012
Earlier work this paper cites.
Tensor factorization using auxiliary information
Atsuhiro Narita, Kohei Hayashi, Ryota Tomioka, and Hisashi Kashima. 2012 · 2012
Earlier work this paper cites.
Alternating Least-Squares for Low-Rank Matrix Reconstruction
Dave Zachariah, Martin Sundin, Magnus Jansson, and Saikat Chatterjee. 2012 · 2012
Earlier work this paper cites.
Provable inductive matrix completion
Prateek Jain and Inderjit S Dhillon. 2013 · 2013
Earlier work this paper cites.
Third-Order Tensors as Operators on Matrices: A Theoretical and Computational Framework with Applications in Imaging
Misha Elena Kilmer, Karen S. Braman, Ning Hao, and Randy C. Hoover. 2013 · 2013
Earlier work this paper cites.
MIRIAD—Public release of a multiple time point Alzheimer’s MR imaging dataset
Ian B Malone, David Cash, Gerard R Ridgway, David G MacManus, Sebastien Ourselin, Nick C Fox, and Jonathan M Schott. 2013 · 2013
Earlier work this paper cites.
Nuclear norm of higher-order tensors
Shmuel Friedland and Lek-Heng Lim. 2014 · 2014
Earlier work this paper cites.
Provable tensor factorization with missing data. In Advances in Neural Information Processing Systems . 1431–1439
Prateek Jain and Sewoong Oh. 2014 · 2014
Earlier work this paper cites.
Novel Methods for Multilinear Data Completion and De-noising Based on Tensor-SVD. In 2014 IEEE Conference on Computer Vision and Pattern Recognition . 3842–3849
Zemin Zhang, Gregory Ely, Shuchin Aeron, Ning Hao, and Misha Kilmer. 2014 · 2014
Earlier work this paper cites.
A new sampling technique for tensors
Srinadh Bhojanapalli and Sujay Sanghavi. 2015 · 2015
Earlier work this paper cites.
Matrix Completion with Noisy Side Information.. In NIPS . 3447–3455
Kai-Yang Chiang, Cho-Jui Hsieh, and Inderjit S Dhillon. 2015 · 2015
Cited alongside, same era.
Symmetric orthogonal tensor decomposition is trivial
Tamara G Kolda. 2015 · 2015
Cited alongside, same era.
Noisy tensor completion via the sum-of-squares hierarchy. In Conference on Learning Theory
Boaz Barak and Ankur Moitra. 2016 · 2016
Cited alongside, same era.
Incorporating side information in tensor completion. In Proceedings of the 25th International Conference Companion on World Wide Web . 65–66
Hemank Lamba, Vaishnavh Nagarajan, Kijung Shin, and Naji Shajarisales. 2016 · 2016
Cited alongside, same era.
Blind regression: Nonparametric regression for latent variable models via collaborative filtering. In Advances in Neural Information Processing Systems . 2155–2163
Dogyoon Song, Christina E Lee, Yihua Li, and Devavrat Shah. 2016 · 2016
Using side information to reliably learn low-rank matrices from missing and corrupted observations
Kai-Yang Chiang, Inderjit S Dhillon, and Cho-Jui Hsieh. 2018 · 2018
Later among the works it cites.
Global optimality in inductive matrix completion. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2226–2230
Mohsen Ghassemi, Anand Sarwate, and Naveen Goela. 2018 · 2018
Later among the works it cites.
Spectral algorithms for tensor completion
Andrea Montanari and Nike Sun. 2018 · 2018
Later among the works it cites.
Inductive framework for multi-aspect streaming tensor completion with side information. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management . 307–316
Madhav Nimishakavi, Bamdev Mishra, Manish Gupta, and Partha Talukdar. 2018 · 2018
Later among the works it cites.
Collective tensor completion with multiple heterogeneous side information. In 2019 IEEE International Conference on Big Data (Big Data) . IEEE, 731–740
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
On tensor completion via nuclear norm minimization
Ming Yuan and Cun-Hui Zhang. 2016 · 2016
Cited alongside, same era.
Exact tensor completion using t-SVD
Zemin Zhang and Shuchin Aeron. 2016 · 2016
Cited alongside, same era.
Thy friend is my friend: Iterative collaborative filtering for sparse matrix estimation. In Advances in Neural Information Processing Systems . 4715–4726
Christian Borgs, Jennifer Chayes, Christina E Lee, and Devavrat Shah. 2017 · 2017
Cited alongside, same era.
Exact tensor completion with sum-of-squares
Aaron Potechin and David Steurer. 2017 · 2017
Cited alongside, same era.
On Polynomial Time Methods for Exact Low-Rank Tensor Completion
Dong Xia and Ming Yuan. 2017 · 2017
Cited alongside, same era.
Statistically optimal and computationally efficient low rank tensor completion from noisy entries
Dong Xia, Ming Yuan, and Cun-Hui Zhang. 2017 · 2017
Cited alongside, same era.
Incoherent tensor norms and their applications in higher order tensor completion
Ming Yuan and Cun-Hui Zhang. 2017 · 2017
Cited alongside, same era.
Huiyuan Chen and Jing Li. 2019 · 2019
Later among the works it cites.
Inference and uncertainty quantification for noisy matrix completion
Yuxin Chen, Jianqing Fan, Cong Ma, and Yuling Yan. 2019 · 2019
Later among the works it cites.
Nearest neighbors for matrix estimation interpreted as blind regression for latent variable model
Yihua Li, Devavrat Shah, Dogyoon Song, and Christina Lee Yu. 2019 · 2019
Later among the works it cites.
Iterative collaborative filtering for sparse noisy tensor estimation. In 2019 IEEE International Symposium on Information Theory (ISIT) . IEEE, 41–45
Devavrat Shah and Christina Lee Yu. 2019 · 2019
Later among the works it cites.
Cross: Efficient low-rank tensor completion
Anru Zhang. 2019 · 2019
Later among the works it cites.
Note: low-rank tensor train completion with side information based on Riemannian optimization
Stanislav Budzinskiy and Nikolai Zamarashkin. 2020 · 2020
Closest in time.
Provable Near-Optimal Low-Multilinear-Rank Tensor Recovery
Jian-Feng Cai, Lizhang Miao, Yang Wang, and Yin Xian. 2020 · 2020
Closest in time.
Iterative collaborative filtering for sparse matrix estimation
Christian Borgs, Jennifer T Chayes, Devavrat Shah, and Christina Lee Yu. 2021 · 2021
Closest in time.
Inductive Matrix Completion with Feature Selection
M Burkina, I Nazarov, M Panov, G Fedonin, and B Shirokikh. 2021 · 2021
Closest in time.
Nonconvex low-rank tensor completion from noisy data
Changxiao Cai, Gen Li, H Vincent Poor, and Yuxin Chen. 2021 · 2021
Closest in time.