| Low-rank matrix completion using alternating minimization P Jain, P Netrapalli, S Sanghavi Proceedings of the forty-fifth annual ACM symposium on Theory of computing …, 2013 | 1261 | 2013 |
| How to escape saddle points efficiently C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan International conference on machine learning, 1724-1732, 2017 | 1031 | 2017 |
| Morel: Model-based offline reinforcement learning R Kidambi, A Rajeswaran, P Netrapalli, T Joachims Advances in neural information processing systems 33, 21810-21823, 2020 | 784 | 2020 |
| Phase retrieval using alternating minimization P Netrapalli, P Jain, S Sanghavi Advances in Neural Information Processing Systems 26, 2013 | 747 | 2013 |
| What is local optimality in nonconvex-nonconcave minimax optimization? C Jin, P Netrapalli, M Jordan International conference on machine learning, 4880-4889, 2020 | 481* | 2020 |
| The pitfalls of simplicity bias in neural networks H Shah, K Tamuly, A Raghunathan, P Jain, P Netrapalli Advances in Neural Information Processing Systems 33, 9573-9585, 2020 | 403 | 2020 |
| Non-convex robust PCA P Netrapalli, N UN, S Sanghavi, A Anandkumar, P Jain Advances in neural information processing systems 27, 2014 | 372 | 2014 |
| Accelerated gradient descent escapes saddle points faster than gradient descent C Jin, P Netrapalli, MI Jordan Conference On Learning Theory, 1042-1085, 2018 | 296 | 2018 |
| On nonconvex optimization for machine learning: Gradients, stochasticity, and saddle points C Jin, P Netrapalli, R Ge, SM Kakade, MI Jordan Journal of the ACM (JACM) 68 (2), 1-29, 2021 | 250* | 2021 |
| Learning the graph of epidemic cascades P Netrapalli, S Sanghavi ACM SIGMETRICS Performance Evaluation Review 40 (1), 211-222, 2012 | 234 | 2012 |
| Efficient algorithms for smooth minimax optimization KK Thekumparampil, P Jain, P Netrapalli, S Oh Advances in Neural Information Processing Systems 32, 2019 | 230 | 2019 |
| Efficient domain generalization via common-specific low-rank decomposition V Piratla, P Netrapalli, S Sarawagi International conference on machine learning, 7728-7738, 2020 | 229 | 2020 |
| Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification P Jain, SM Kakade, R Kidambi, P Netrapalli, A Sidford Journal of machine learning research 18 (223), 1-42, 2018 | 226* | 2018 |
| 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 | 201 | 2016 |
| The step decay schedule: A near optimal, geometrically decaying learning rate procedure for least squares R Ge, SM Kakade, R Kidambi, P Netrapalli Advances in neural information processing systems 32, 2019 | 199 | 2019 |
| A short note on concentration inequalities for random vectors with subgaussian norm C Jin, P Netrapalli, R Ge, SM Kakade, MI Jordan arXiv preprint arXiv:1902.03736, 2019 | 173 | 2019 |
| Streaming pca: Matching matrix bernstein and near-optimal finite sample guarantees for oja’s algorithm P Jain, C Jin, SM Kakade, P Netrapalli, A Sidford Conference on learning theory, 1147-1164, 2016 | 161 | 2016 |
| Information-theoretic thresholds for community detection in sparse networks J Banks, C Moore, J Neeman, P Netrapalli Conference on Learning Theory, 383-416, 2016 | 152* | 2016 |
| Accelerating stochastic gradient descent for least squares regression P Jain, SM Kakade, R Kidambi, P Netrapalli, A Sidford Conference On Learning Theory, 545-604, 2018 | 143 | 2018 |
| On the insufficiency of existing momentum schemes for stochastic optimization R Kidambi, P Netrapalli, P Jain, S Kakade 2018 Information Theory and Applications Workshop (ITA), 1-9, 2018 | 141 | 2018 |
Prateek JainGoogle Research India確認したメール アドレス: google.com