Fetching the paper…
Reading the bibliography…
In this paper, we propose a general framework for sparse and low-rank tensor estimation from cubic sketchings.
New York: Cambridge Univ. Press, 1988
R. A. Horn and C. R. Johnson, Matrix Analysis · 1988
Earlier work this paper cites.
M. Talagrand, “The supremum of some canonical processes,” American Journal of Mathematics
1994
Earlier work this paper cites.
P. Hitczenko, S. Montgomery-Smith, and K. Oleszkiewicz, “Moment inequalities for sums of certain independent symmetric random variables,” Studia Math
1997
Earlier work this paper cites.
B. Yu, “Assouad, fano, and le cam,” Festschrift for Lucien Le Cam
1997
Earlier work this paper cites.
M. A. O. Vasilescu and D. Terzopoulos, “Multilinear subspace analysis of image ensembles,” in Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on
2003
Earlier work this paper cites.
C. Stein, P. Diaconis, S. Holmes, G. Reinert, et al
2004
Earlier work this paper cites.
No. 89, American Mathematical Soc., 2005
M. Ledoux, The concentration of measure phenomenon · 2005
Earlier work this paper cites.
D. L. Donoho, “Compressed sensing,” IEEE Transactions on information theory
2006
Earlier work this paper cites.
Wiley Series in Probability and Statistics, 2008
P. M. Kroonenberg, Applied Multiway Data Analysis · 2008
Earlier work this paper cites.
T. Kolda and B. Bader, “Tensor decompositions and applications,” SIAM Review
2009
Earlier work this paper cites.
E. J. Candès and B. Recht, “Exact matrix completion via convex optimization,” Foundations of Computational mathematics
2009
Earlier work this paper cites.
N. Li and B. Li, “Tensor completion for on-board compression of hyperspectral images,” in 2010 IEEE International Conference on Image Processing
2010
Earlier work this paper cites.
R. H. Keshavan, A. Montanari, and S. Oh, “Matrix completion from a few entries,” IEEE Transactions on Information Theory
2010
Earlier work this paper cites.
R. Adamczak, A. E. Litvak, A. Pajor, and N. Tomczak-Jaegermann, “Restricted isometry property of matrices with independent columns and neighborly polytopes by random sampling,” Constructive Approximation
2011
Earlier work this paper cites.
V. Koltchinskii, K. Lounici, and A. B. Tsybakov, “Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion,” The Annals of Statistics
2011
Earlier work this paper cites.
V. Chandrasekaran, S. Sanghavi, P. A. Parrilo, and A. S. Willsky, “Rank-sparsity incoherence for matrix decomposition,” SIAM Journal on Optimization
2011
Earlier work this paper cites.
N. D. Sidiropoulos and A. Kyrillidis, “Multi-way compressed sensing for sparse low-rank tensors,” IEEE Signal Processing Letters
2012
Earlier work this paper cites.
Cambridge Univ. Press, 2012
R. Vershynin, Compressed sensing · 2012
Earlier work this paper cites.
Springer Science & Business Media, 2012
V. De la Pena and E. Giné, Decoupling: from dependence to independence · 2012
Earlier work this paper cites.
H. Zhou, L. Li, and H. Zhu, “Tensor regression with applications in neuroimaging data analysis,” Journal of the American Statistical Association
2013
Earlier work this paper cites.
C. F. Caiafa and A. Cichocki, “Multidimensional compressed sensing and their applications,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
2013
Earlier work this paper cites.
J. Liu, P. Musialski, P. Wonka, and J. Ye, “Tensor completion for estimating missing values in visual data,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2013
Earlier work this paper cites.
B. Romera-Paredes, M. H. Aung, N. Bianchi-Berthouze, and M. Pontil, “Multilinear multitask learning,” in Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28
2013
Cited alongside, same era.
J. Bien, J. Taylor, R. Tibshirani, et al
2013
Cited alongside, same era.
C. J. Hillar and L.-H. Lim, “Most tensor problems are np-hard,” Journal of the ACM (JACM)
2013
Cited alongside, same era.
Springer Science & Business Media, 2013
M. Ledoux and M. Talagrand, Probability in Banach Spaces: isoperimetry and processes · 2013
Cited alongside, same era.
S. Friedland, Q. Li, and D. Schonfeld, “Compressive sensing of sparse tensors,” IEEE Transactions on Image Processing
2014
Cited alongside, same era.
Y. Zhang, X. Chen, D. Zhou, and M. I. Jordan, “Spectral methods meet em: A provably optimal algorithm for crowdsourcing,” The Journal of Machine Learning Research
2016
Later among the works it cites.
T. T. Cai, X. Li, and Z. Ma, “Optimal rates of convergence for noisy sparse phase retrieval via thresholded wirtinger flow,” The Annals of Statistics
2016
Later among the works it cites.
H. Hung, Y.-T. Lin, P. Chen, C.-C. Wang, S.-Y. Huang, and J.-Y. Tzeng, “Detection of gene–gene interactions using multistage sparse and low-rank regression,” Biometrics
2016
Later among the works it cites.
S. Tu, R. Boczar, M. Simchowitz, M. Soltanolkotabi, and B. Recht, “Low-rank solutions of linear matrix equations via procrustes flow,” in Proceedings of The 33rd International Conference on Machine Learning
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2014
Cited alongside, same era.
Z. Wang, H. Liu, and T. Zhang, “Optimal computational and statistical rates of convergence for sparse nonconvex learning problems,” The Annals of statistics
2014
Cited alongside, same era.
E. Richard and A. Montanari, “A statistical model for tensor pca,” in Advances in Neural Information Processing Systems 27
2014
Cited alongside, same era.
C. Mu, B. Huang, J. Wright, and D. Goldfarb, “Square deal: Lower bounds and improved relaxations for tensor recovery,” in Proceedings of the 31st International Conference on Machine Learning
2014
Cited alongside, same era.
A. Anandkumar, R. Ge, D. Hsu, S. M. Kakade, and M. Telgarsky, “Tensor decompositions for learning latent variable models,” Journal of Machine Learning Research
2014
Cited alongside, same era.
S. Arora, R. Ge, and A. Moitra, “New algorithms for learning incoherent and overcomplete dictionaries,” in Proceedings of The 27th Conference on Learning Theory
2014
Cited alongside, same era.
2014
Cited alongside, same era.
2017
Later among the works it cites.
M. Yuan and C.-H. Zhang, “Incoherent tensor norms and their applications in higher order tensor completion,” IEEE Transactions on Information Theory
2017
Later among the works it cites.
J. A. Bengua, H. N. Phien, H. D. Tuan, and M. N. Do, “Efficient tensor completion for color image and video recovery: Low-rank tensor train,” IEEE Transactions on Image Processing
2017
Later among the works it cites.
L. Li and X. Zhang, “Parsimonious tensor response regression,” Journal of the American Statistical Association
2017
Later among the works it cites.
W. W. Sun, J. Lu, H. Liu, and G. Cheng, “Provable sparse tensor decomposition,” Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2017
Later among the works it cites.
H. Rauhut, R. Schneider, and Ž. Stojanac, “Low rank tensor recovery via iterative hard thresholding,” Linear Algebra and its Applications
2017
Later among the works it cites.
X. Li, J. Haupt, and D. Woodruff, “Near optimal sketching of low-rank tensor regression,” in Advances in Neural Information Processing Systems
2017
Later among the works it cites.
X. Li, D. Xu, H. Zhou, and L. Li, “Tucker tensor regression and neuroimaging analysis,” Statistics in Biosciences
2018
Closest in time.
A. Montanari and N. Sun, “Spectral algorithms for tensor completion,” Communications on Pure and Applied Mathematics
2018
Closest in time.
N. Ghadermarzy, Y. Plan, and Ö. Yilmaz, “Near-optimal sample complexity for convex tensor completion,” Information and Inference: A Journal of the IMA
2018
Closest in time.
S. Basu, K. Kumbier, J. B. Brown, and B. Yu, “Iterative random forests to discover predictive and stable high-order interactions,” Proceedings of the National Academy of Sciences
2018
Closest in time.
A. Zhang and D. Xia, “Tensor SVD: Statistical and computational limits,” IEEE Transactions on Information Theory
2018
Closest in time.
S. Friedland and L.-H. Lim, “Nuclear norm of higher-order tensors,” Mathematics of Computation
2018
Closest in time.
A. Zhang, “Cross: Efficient low-rank tensor completion,” The Annals of Statistics
2019
Closest in time.
G. Raskutti, M. Yuan, H. Chen, et al
2019
Closest in time.
H. Chen, G. Raskutti, and M. Yuan, “Non-convex projected gradient descent for generalized low-rank tensor regression,” The Journal of Machine Learning Research
2019
Closest in time.
2019
Closest in time.