| Tensor decompositions for learning latent variable models A Anandkumar, R Ge, D Hsu, SM Kakade, M Telgarsky arXiv preprint arXiv:1210.7559 64, 70-72, 0 | 596* | |
| A method of moments for mixture models and hidden Markov models A Anandkumar, D Hsu, SM Kakade Conference on Learning Theory, 33.1-33.34, 2012 | 230 | 2012 |
| A spectral algorithm for latent dirichlet allocation A Anandkumar, DP Foster, DJ Hsu, SM Kakade, YK Liu Advances in Neural Information Processing Systems, 917-925, 2012 | 214 | 2012 |
| Distributed algorithms for learning and cognitive medium access with logarithmic regret A Anandkumar, N Michael, AK Tang, A Swami IEEE Journal on Selected Areas in Communications 29 (4), 731-745, 2011 | 188 | 2011 |
| Learning latent tree graphical models MJ Choi, VYF Tan, A Anandkumar, AS Willsky Journal of Machine Learning Research 12 (May), 1771-1812, 2011 | 184 | 2011 |
| A tensor approach to learning mixed membership community models A Anandkumar, R Ge, D Hsu, SM Kakade The Journal of Machine Learning Research 15 (1), 2239-2312, 2014 | 175 | 2014 |
| Learning sparsely used overcomplete dictionaries A Agarwal, A Anandkumar, P Jain, P Netrapalli, R Tandon Conference on Learning Theory, 123-137, 2014 | 167 | 2014 |
| Opportunistic spectrum access with multiple users: Learning under competition A Anandkumar, N Michael, A Tang INFOCOM, 2010 Proceedings IEEE, 1-9, 2010 | 158 | 2010 |
| Non-convex robust PCA P Netrapalli, UN Niranjan, S Sanghavi, A Anandkumar, P Jain Advances in Neural Information Processing Systems, 1107-1115, 2014 | 153 | 2014 |
| 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 | 123* | 2015 |
| High-dimensional structure estimation in Ising models: Local separation criterion A Anandkumar, VYF Tan, F Huang, AS Willsky The Annals of Statistics 40 (3), 1346-1375, 2012 | 96* | 2012 |
| Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank- Updates A Anandkumar, R Ge, M Janzamin arXiv preprint arXiv:1402.5180, 2014 | 77 | 2014 |
| Two SVDs Su ffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation YK Liu, A Anandkumar, DP Foster, D Hsu, SM Kakade Neural Information Processing Systems (NIPS), 2012 | 74 | 2012 |
| Online tensor methods for learning latent variable models F Huang, UN Niranjan, MU Hakeem, A Anandkumar The Journal of Machine Learning Research 16 (1), 2797-2835, 2015 | 66* | 2015 |
| High-dimensional Gaussian graphical model selection: Walk summability and local separation criterion A Anandkumar, VYF Tan, F Huang, AS Willsky Journal of Machine Learning Research 13 (Aug), 2293-2337, 2012 | 60* | 2012 |
| A large-deviation analysis of the maximum-likelihood learning of Markov tree structures VYF Tan, A Anandkumar, L Tong, AS Willsky IEEE Transactions on Information Theory 57 (3), 1714-1735, 2011 | 57 | 2011 |
| Efficient approaches for escaping higher order saddle points in non-convex optimization A Anandkumar, R Ge Conference on Learning Theory, 81-102, 2016 | 56 | 2016 |
| Learning topic models and latent Bayesian networks under expansion constraints A Anandkumar, D Hsu, A Javanmard, SM Kakade arXiv preprint arXiv:1209.5350, 2012 | 54* | 2012 |
| Detection of Gauss–Markov random fields with nearest-neighbor dependency A Anandkumar, L Tong, A Swami IEEE Transactions on Information Theory 55 (2), 816-827, 2009 | 51 | 2009 |
| Type-based random access for distributed detection over multiaccess fading channels A Anandkumar, L Tong IEEE Transactions on Signal Processing 55 (10), 5032-5043, 2007 | 50 | 2007 |