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In this paper, we resolve many of the key algorithmic questions regarding robustness, memory efficiency, and differential privacy of tensor decomposition.
Norms of gaussian sample functions
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Matrix perturbation theory
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L. Birgé · 2001
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Algorithm 862: Matlab tensor classes for fast algorithm prototyping
B. W. Bader and T. G. Kolda · 2006
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Concentration inequalities and model selection
P. Massart · 2007
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Shifted power method for computing tensor eigenpairs
T. G. Kolda and J. R. Mayo · 2011
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A spectral algorithm for latent dirichlet allocation
A. Anandkumar, Y.-k. Liu, D. J. Hsu, D. P. Foster, and S. M. Kakade · 2012
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A tail inequality for quadratic forms of subgaussian random vectors
D. Hsu, S. M. Kakade, and T. Zhang · 2012
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Most tensor problems are np-hard
C. J. Hillar and L.-H. Lim · 2013
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Learning fair representations
R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
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Tensor decompositions for learning latent variable models
A. Anandkumar, R. Ge, D. Hsu, S. M. Kakade, and M. Telgarsky · 2014
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The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
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Analyze gauss: optimal bounds for privacy-preserving principal component analysis
C. Dwork, K. Talwar, A. Thakurta, and L. Zhang · 2014
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The noisy power method: A meta algorithm with applications
M. Hardt and E. Price · 2014
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Scalable latent tree model and its application to health analytics
F. Huang, I. Perros, R. Chen, J. Sun, A. Anandkumar, et al · 2014
Escaping from saddle points—online stochastic gradient for tensor decomposition
R. Ge, F. Huang, C. Jin, and Y. Yuan · 2015
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Tensor principal component analysis via sum-of-squares proofs
S. B. Hopkins, J. Shi, and D. Steurer · 2015
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Online tensor methods for learning latent variable models
F. Huang, U. Niranjan, M. U. Hakeem, and A. Anandkumar · 2015
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Beating the perils of non-convexity: Guaranteed training of neural networks using tensor methods
M. Janzamin, H. Sedghi, and A. Anandkumar · 2015
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Tensor factorization via matrix factorization
V. Kuleshov, A. T. Chaganty, and P. Liang · 2015
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Successive rank-one approximations for nearly orthogonally decomposable symmetric tensors
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A statistical model for tensor PCA
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Spectral norm of random tensors
R. Tomioka and T. Suzuki · 2014
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Spectral methods for supervised topic models
Y. Wang and J. Zhu · 2014
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Learning overcomplete latent variable models through tensor methods
A. Anandkumar, R. Ge, and M. Janzamin · 2015
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C. Mu, D. Hsu, and D. Goldfarb · 2015
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Fast and guaranteed tensor decomposition via sketching
Y. Wang, H.-Y. Tung, A. J. Smola, and A. Anandkumar · 2015
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Reinforcement learning of POMDP’s using spectral methods
K. Azizzadenesheli, A. Lazaric, and A. Anandkumar · 2016
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An improved gap-dependency analysis of the noisy power method
M.-F. Balcan, S. Du, Y. Wang, and A. W. Yu · 2016
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Bounds on the expectation of the maximum of samples from a gaussian
G. Kamath · 2016
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