| MOReL: Model-based Offline Reinforcement Learning R Kidambi, A Rajeswaran, P Netrapalli, T Joachims Conference on Neural Information Processing Systems (NeurIPS), 2020, 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 (JMLR), 2018, 2018 | 135* | 2018 |
| Accelerating stochastic gradient descent for least squares regression P Jain, SM Kakade, R Kidambi, P Netrapalli, A Sidford Conference On Learning Theory (COLT), 2018, 2018 | 122* | 2018 |
| The step decay schedule: A near optimal, geometrically decaying learning rate procedure R Ge, SM Kakade, R Kidambi, P Netrapalli Conference on Neural Information Processing Systems (NeurIPS), 2019, 2019 | 91* | 2019 |
| On the insufficiency of existing momentum schemes for Stochastic Optimization R Kidambi, P Netrapalli, P Jain, SM Kakade International Conference on Learning Representations (ICLR), 2018, 2018 | 80 | 2018 |
| A markov chain theory approach to characterizing the minimax optimality of stochastic gradient descent (for least squares) P Jain, SM Kakade, R Kidambi, P Netrapalli, VK Pillutla, A Sidford FSTTCS (invited), 2017 | 21 | 2017 |
| Submodular hamming metrics J Gillenwater, R Iyer, B Lusch, R Kidambi, J Bilmes Conference on Neural Information Processing Systems (NeurIPS), 2015 | 19 | 2015 |
| Leverage score sampling for faster accelerated regression and ERM N Agarwal, S Kakade, R Kidambi, YT Lee, P Netrapalli, A Sidford Algorithmic Learning Theory (ALT), 2020, 2020 | 16 | 2020 |
| Making Paper Reviewing Robust to Bid Manipulation Attacks R Wu, C Guo, F Wu, R Kidambi, L van der Maaten, KQ Weinberger International Conference on Machine Learning (ICML) 2021, 2021 | 8 | 2021 |
| Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage JD Chang, M Uehara, D Sreenivas, R Kidambi, W Sun Conference on Neural Information Processing Systems (NeurIPS) 2021, 2021 | 7* | 2021 |
| Top- eXtreme Contextual Bandits with Arm Hierarchy R Sen, A Rakhlin, L Ying, R Kidambi, D Foster, D Hill, I Dhillon International Conference on Machine Learning (ICML) 2021, 2021 | 7 | 2021 |
| Deformable trellises on factor graphs for robust microtubule tracking in clutter R Kidambi, MC Shih, K Rose IEEE International Symposium on Biomedical Imaging (ISBI), 2012, 2012 | 5 | 2012 |
| MobILE: Model-Based Imitation Learning From Observation Alone R Kidambi, J Chang, W Sun Conference on Neural Information Processing Systems (NeurIPS) 2021, 2021 | 4* | 2021 |
| Efficient estimation of generalization error and bias-variance components of ensembles D Mahajan, V Gupta, SS Keerthi, S Sundararajan, S Narayanamurthy, ... arXiv preprint arXiv:1711.05482, 2017 | 3 | 2017 |
| 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 (COLT), 2019, 2019 | 2 | 2019 |
| Stochastic Gradient Descent For Modern Machine Learning: Theory, Algorithms And Applications R Kidambi University of Washington Seattle, 2019 | 1 | 2019 |
| On shannon capacity and causal estimation R Kidambi, S Kannan Annual Allerton Conference on Communication, Control, and Computing …, 2015 | 1 | 2015 |
| Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion A Block, R Kidambi, DN Hill, T Joachims, IS Dhillon arXiv preprint arXiv:2204.10936, 2022 | | 2022 |
| A Quantitative Evaluation Framework for Missing Value Imputation Algorithms V Nair, R Kidambi, S Sellamanickam, SS Keerthi, J Gehrke, V Narayanan arXiv preprint arXiv:1311.2276, 2013 | | 2013 |
| A Structured Prediction Approach for Missing Value Imputation R Kidambi, V Nair, S Sellamanickam, SS Keerthi arXiv preprint arXiv:1311.2137, 2013 | | 2013 |
Praneeth NetrapalliGoogle ResearchVerified email at google.com
