| 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 | 882* | |
| 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 | 288 | 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 | 270 | 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 | 268 | 2011 |
| Non-convex robust PCA P Netrapalli, UN Niranjan, S Sanghavi, A Anandkumar, P Jain Advances in Neural Information Processing Systems, 1107-1115, 2014 | 242 | 2014 |
| Born again neural networks T Furlanello, ZC Lipton, M Tschannen, L Itti, A Anandkumar arXiv preprint arXiv:1805.04770, 2018 | 240 | 2018 |
| 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 | 229 | 2014 |
| Learning latent tree graphical models MJ Choi, VYF Tan, A Anandkumar, AS Willsky Journal of Machine Learning Research 12, 1771-1812, 2011 | 225 | 2011 |
| Stochastic activation pruning for robust adversarial defense GS Dhillon, K Azizzadenesheli, ZC Lipton, J Bernstein, J Kossaifi, ... arXiv preprint arXiv:1803.01442, 2018 | 222 | 2018 |
| 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 | 191 | 2015 |
| signSGD: Compressed optimisation for non-convex problems J Bernstein, YX Wang, K Azizzadenesheli, A Anandkumar arXiv preprint arXiv:1802.04434, 2018 | 176 | 2018 |
| Opportunistic spectrum access with multiple users: Learning under competition A Anandkumar, N Michael, A Tang 2010 Proceedings IEEE INFOCOM, 1-9, 2010 | 173 | 2010 |
| Learning sparsely used overcomplete dictionaries via alternating minimization A Agarwal, A Anandkumar, P Jain, P Netrapalli SIAM Journal on Optimization 26 (4), 2775-2799, 2016 | 147 | 2016 |
| Deep active learning for named entity recognition Y Shen, H Yun, ZC Lipton, Y Kronrod, A Anandkumar arXiv preprint arXiv:1707.05928, 2017 | 138 | 2017 |
| 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 | 118* | 2012 |
| Efficient approaches for escaping higher order saddle points in non-convex optimization A Anandkumar, R Ge Conference on learning theory, 81-102, 2016 | 108 | 2016 |
| Tensorly: Tensor learning in python J Kossaifi, Y Panagakis, A Anandkumar, M Pantic The Journal of Machine Learning Research 20 (1), 925-930, 2019 | 102 | 2019 |
| Learning sparsely used overcomplete dictionaries A Agarwal, A Anandkumar, P Jain, P Netrapalli, R Tandon Conference on Learning Theory, 123-137, 2014 | 101 | 2014 |
| Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank- Updates A Anandkumar, R Ge, M Janzamin arXiv preprint arXiv:1402.5180, 2014 | 99 | 2014 |
| High-dimensional Gaussian graphical model selection: Walk summability and local separation criterion A Anandkumar, VYF Tan, F Huang, AS Willsky The Journal of Machine Learning Research 13 (1), 2293-2337, 2012 | 89* | 2012 |
