| Beating the perils of non-convexity: Guaranteed training of neural networks using tensor methods M Janzamin, H Sedghi, A Anandkumar arXiv preprint arXiv:1506.08473, 2015 | 280 | 2015 |
| Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank- Updates A Anandkumar, R Ge, M Janzamin arXiv preprint arXiv:1402.5180, 2014 | 157 | 2014 |
| Provable tensor methods for learning mixtures of generalized linear models H Sedghi, M Janzamin, A Anandkumar Artificial Intelligence and Statistics, 1223-1231, 2016 | 110 | 2016 |
| When are overcomplete topic models identifiable? uniqueness of tensor tucker decompositions with structured sparsity A Anandkumar, DJ Hsu, M Janzamin, SM Kakade Advances in neural information processing systems 26, 2013 | 61 | 2013 |
| Learning overcomplete latent variable models through tensor methods A Anandkumar, R Ge, M Janzamin Conference on Learning Theory, 36-112, 2015 | 60 | 2015 |
| Analyzing tensor power method dynamics in overcomplete regime A Anandkumar, R Ge, M Janzamin Journal of Machine Learning Research 18 (22), 1-40, 2017 | 55 | 2017 |
| Spectral learning on matrices and tensors M Janzamin, R Ge, J Kossaifi, A Anandkumar Foundations and Trends® in Machine Learning 12 (5-6), 393-536, 2019 | 52 | 2019 |
| Score function features for discriminative learning: Matrix and tensor framework M Janzamin, H Sedghi, A Anandkumar arXiv preprint arXiv:1412.2863, 2014 | 48 | 2014 |
| Analyzing tensor power method dynamics: Applications to learning overcomplete latent variable models A Anandkumar, R Ge, M Janzamin arXiv preprint arXiv:1411.1488 98, 2014 | 30 | 2014 |
| Sample complexity analysis for learning overcomplete latent variable models through tensor methods A Anandkumar, R Ge, M Janzamin arXiv preprint arXiv:1408.0553, 2014 | 30* | 2014 |
| A game-theoretic approach for power allocation in bidirectional cooperative communication M Janzamin, MR Pakravan, H Sedghi 2010 IEEE Wireless Communication and Networking Conference, 1-6, 2010 | 29 | 2010 |
| High-dimensional covariance decomposition into sparse Markov and independence models M Janzamin, A Anandkumar The Journal of Machine Learning Research 15 (1), 1549-1591, 2014 | 9 | 2014 |
| High-dimensional covariance decomposition into sparse Markov and independence domains M Janzamin, A Anandkumar arXiv preprint arXiv:1206.6382, 2012 | 4 | 2012 |
| Feast at play: Feature extraction using score function tensors M Janzamin, H Sedghi, UN Niranjan, A Anandkumar Feature Extraction: Modern Questions and Challenges, 130-144, 2015 | 2 | 2015 |
| Score function features for discriminative learning M Janzamin, H Sedghi, A Anandkumar arXiv preprint arXiv:1412.6514, 2014 | 1 | 2014 |
| Non-convex Optimization in Machine Learning: Provable Guarantees Using Tensor Methods M Janzamin University of California, Irvine, 2016 | | 2016 |
| Matrix and Tensor Features for Discriminative Learning M Janzamin, H Sedghi, A Anandkumar arXiv preprint arXiv:1412.2863, 2014 | | 2014 |
| Supplementary Material for the AISTATS 2016 Paper: Provable Tensor Methods for Learning Mixtures of Generalized Linear Models H Sedghi, M Janzamin, A Anandkumar | | |
| When are Overcomplete Representations Identifiable? Uniqueness of Tensor Decompositions Under Expansion Constraints A Anandkumar, D Hsu, M Janzamin, S Kakade | | |
Anima AnandkumarCalifornia Institute of Technology and NVIDIAVerified email at caltech.edu
