| Gradient coding: Avoiding stragglers in distributed learning R Tandon, Q Lei, AG Dimakis, N Karampatziakis International Conference on Machine Learning, 3368-3376, 2017 | 527* | 2017 |
| Learning sparsely used overcomplete dictionaries A Agarwal, A Anandkumar, P Jain, P Netrapalli, R Tandon Conference on Learning Theory, 123-137, 2014 | 119 | 2014 |
| Gradient coding from cyclic mds codes and expander graphs N Raviv, I Tamo, R Tandon, AG Dimakis arXiv preprint arXiv:1707.03858, 2017 | 74 | 2017 |
| Sparse nonnegative matrix approximation: new formulations and algorithms R Tandon, S Sra Max Planck Institute for Biological Cybernetics, 2010 | 43 | 2010 |
| On the information theoretic limits of learning Ising models R Tandon, K Shanmugam, PK Ravikumar, AG Dimakis Advances in Neural Information Processing Systems, 2303-2311, 2014 | 41 | 2014 |
| On the difficulty of learning power law graphical models R Tandon, P Ravikumar 2013 IEEE International Symposium on Information Theory, 2493-2497, 2013 | 17 | 2013 |
| Kernel Ridge Regression via Partitioning R Tandon, S Si, P Ravikumar, I Dhillon arXiv preprint arXiv:1608.01976, 2016 | 12 | 2016 |
| Learning Graphs with a Few Hubs R Tandon, P Ravikumar Proceedings of The 31st International Conference on Machine Learning, 602-610, 2014 | 8 | 2014 |
| On the difficulty of learning power law graphical models: Proofs R Tandon, P Ravikumar | 1 | |
| Recovering sparsely used overcomplete dictionaries via alternating minimization A Agarwal, A Anandkumar, P Jain, P Netrapalli, R Tandon | | |
Pradeep RavikumarProfessor, School of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
