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We give an algorithm for completing an order-$m$ symmetric low-rank tensor from its multilinear entries in time roughly proportional to the number of tensor entries.
Renè Peeters, Orthogonal representations over finite fields and the chromatic number of graphs , Combinatorica 16
1996
Earlier work this paper cites.
Yoav Freund and Yishay Mansour, Estimating a mixture of two product distributions , Proceedings of the Twelfth Annual Conference on Computational Learning Theory, COLT Santa Cruz, CA, USA, July 7-9, 1999, pp. 53–62
1999
Earlier work this paper cites.
Mary Cryan, Leslie Ann Goldberg, and Paul W. Goldberg, Evolutionary trees can be learned in polynomial time in the two-state general markov model , SIAM J. Comput. 31
2001
Earlier work this paper cites.
Elchanan Mossel, Ryan O’Donnell, and Rocco A. Servedio, Learning functions of k relevant variables , J. Comput. Syst. Sci. 69
2004
Earlier work this paper cites.
Dimitris Achlioptas and Frank McSherry, On spectral learning of mixtures of distributions , Learning Theory, 18th Annual Conference on Learning Theory, COLT, 2005, pp. 458–469
2005
Earlier work this paper cites.
Elchanan Mossel and Sébastien Roch, Learning nonsingular phylogenies and hidden markov models , The Annals of Applied Probability 16
2006
Earlier work this paper cites.
Vitaly Feldman, Attribute-efficient and non-adaptive learning of parities and DNF expressions , Journal of Machine Learning Research 8
2007
Earlier work this paper cites.
Lieven De Lathauwer, Joséphine Castaing, and Jean-François Cardoso, Fourth-order cumulant-based blind identification of underdetermined mixtures , IEEE Transactions on Signal Processing 55
2007
Earlier work this paper cites.
Kamalika Chaudhuri and Satish Rao, Learning mixtures of product distributions using correlations and independence , 21st Annual Conference on Learning Theory - COLT 2008, Helsinki, Finland, July 9-12, 2008, 2008, pp. 9–20
2008
Earlier work this paper cites.
Jon Feldman, Ryan O’Donnell, and Rocco A. Servedio, Learning mixtures of product distributions over discrete domains , SIAM J. Comput. 37
2008
Earlier work this paper cites.
Emmanuel J. Candès and Yaniv Plan, Matrix completion with noise , CoRR abs/0903.3131
2009
Earlier work this paper cites.
Emmanuel J. Candès and Benjamin Recht, Exact matrix completion via convex optimization , Foundations of Computational Mathematics 9
2009
Cited alongside, same era.
Benjamin Recht, A simpler approach to matrix completion , CoRR abs/0910.0651
2009
Cited alongside, same era.
Oded Regev, On lattices, learning with errors, random linear codes, and cryptography , J. ACM 56
2009
Cited alongside, same era.
Daniel Hsu, Sham M. Kakade, and Tong Zhang, Robust matrix decomposition with sparse corruptions , IEEE Transactions on Information Theory 57
2011
Cited alongside, same era.
Animashree Anandkumar, Daniel Hsu, and Sham M. Kakade, A method of moments for mixture models and hidden markov models , COLT 2012 - The 25th Annual Conference on Learning Theory, June 25-27, 2012, Edinburgh, Scotland, 2012, pp. 33.1–33.34
2014
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2014
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Aditya Bhaskara, Moses Charikar, Ankur Moitra, and Aravindan Vijayaraghavan, Smoothed analysis of tensor decompositions , Symposium on Theory of Computing, STOC, 2014
2014
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Moritz Hardt, Understanding alternating minimization for matrix completion , 55th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2014, Philadelphia, PA, USA, October 18-21, 2014, 2014, pp. 651–660
2014
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2012
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Suriya Gunasekar, Ayan Acharya, Neeraj Gaur, and Joydeep Ghosh, Noisy matrix completion using alternating minimization , Machine Learning and Knowledge Discovery in Databases (Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, and Filip Železný, eds.), Lecture Notes in Computer Science, vol. 8189, Springer Berlin Heidelberg, 2013, pp. 194–209 (English)
2013
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Daniel Hsu and Sham M. Kakade, Learning mixtures of spherical gaussians: moment methods and spectral decompositions , Innovations in Theoretical Computer Science, ITCS ’13, Berkeley, CA, USA, January 9-12, 2013, 2013, pp. 11–20
2013
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2013
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Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, and Matus Telgarsky, Tensor decompositions for learning latent variable models , Journal of Machine Learning Research 15
2014
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Animashree Anandkumar, Rong Ge, Daniel Hsu, and Sham M. Kakade, A tensor approach to learning mixed membership community models , Journal of Machine Learning Research 15
2014
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Ph.D. thesis
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Richard A. Harshman, Foundations of the PARFAC Procedure: Models and Conditions for and “Explanatory” Multimodal Factor Analysis
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2014
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Prateek Jain and Sewoong Oh, Provable tensor factorization with missing data , Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, 2014, pp. 1431–1439
2014
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Boaz Barak, Jonathan A. Kelner, and David Steurer, Dictionary learning and tensor decomposition via the sum-of-squares method , Proceedings of the Forty-Seventh Annual ACM on Symposium on Theory of Computing, STOC 2015, Portland, OR, USA, June 14-17, 2015, 2015, pp. 143–151
2015
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2015
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2015
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