| Gradient coding: Avoiding stragglers in distributed learning R Tandon, Q Lei, AG Dimakis, N Karampatziakis International Conference on Machine Learning, 3368-3376, 2017 | 629* | 2017 |
| Gradient coding from cyclic MDS codes and expander graphs N Raviv, R Tandon, A Dimakis, I Tamo International Conference on Machine Learning, 4305-4313, 2018 | 216 | 2018 |
| Learning sparsely used overcomplete dictionaries A Agarwal, A Anandkumar, P Jain, P Netrapalli, R Tandon Conference on Learning Theory, 123-137, 2014 | 122 | 2014 |
| Sparse nonnegative matrix approximation: new formulations and algorithms R Tandon, S Sra Max Planck Institute for Biological Cybernetics, 2010 | 50 | 2010 |
| On the information theoretic limits of learning Ising models R Tandon, K Shanmugam, PK Ravikumar, AG Dimakis Advances in Neural Information Processing Systems 27, 2014 | 44 | 2014 |
| On the difficulty of learning power law graphical models R Tandon, P Ravikumar 2013 IEEE International Symposium on Information Theory, 2493-2497, 2013 | 18 | 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 | 9 | 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 UniversityПотвърден имейл адрес: cs.cmu.edu
