| SGD without Replacement: Sharper Rates for General Smooth Convex Functions D Nagaraj, P Jain, P Netrapalli International Conference on Machine Learning, 4703-4711, 2019 | 19* | 2019 |
| Making the last iterate of sgd information theoretically optimal P Jain, D Nagaraj, P Netrapalli arXiv preprint arXiv:1904.12443, 2019 | 19 | 2019 |
| Continuous limit of discrete quantum walks MN Dheeraj, TA Brun Physical Review A 91 (6), 062304, 2015 | 16 | 2015 |
| Optimal Single Sample Tests for Structured versus Unstructured Network Data G Bresler, D Nagaraj arXiv preprint arXiv:1802.06186, 2018 | 14 | 2018 |
| Stein’s method for stationary distributions of Markov chains and application to Ising models G Bresler, D Nagaraj The Annals of Applied Probability 29 (5), 3230-3265, 2019 | 6 | 2019 |
| A corrective view of neural networks: Representation, memorization and learning G Bresler, D Nagaraj arXiv preprint arXiv:2002.00274, 2020 | 4 | 2020 |
| Phase transitions for detecting latent geometry in random graphs M Brennan, G Bresler, D Nagaraj Probability Theory and Related Fields 178 (3), 1215-1289, 2020 | 3 | 2020 |
| Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth G Bresler, D Nagaraj arXiv preprint arXiv:2006.04048, 2020 | 1 | 2020 |
| Open Problem: Do Good Algorithms Necessarily Query Bad Points? R Ge, P Jain, SM Kakade, R Kidambi, DM Nagaraj, P Netrapalli Conference on Learning Theory, 3190-3193, 2019 | 1 | 2019 |
| A law of robustness for two-layers neural networks S Bubeck, Y Li, D Nagaraj arXiv preprint arXiv:2009.14444, 2020 | | 2020 |
| Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms G Bresler, P Jain, D Nagaraj, P Netrapalli, X Wu arXiv preprint arXiv:2006.08916, 2020 | | 2020 |
Praneeth NetrapalliMicrosoft ResearchVerified email at microsoft.com
