| Dual averaging for distributed optimization: Convergence analysis and network scaling JC Duchi, A Agarwal, MJ Wainwright IEEE Transactions on Automatic control 57 (3), 592-606, 2011 | 1096 | 2011 |
| Distributed delayed stochastic optimization A Agarwal, JC Duchi 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 5451-5452, 2012 | 577 | 2012 |
| A reductions approach to fair classification A Agarwal, A Beygelzimer, M Dudík, J Langford, H Wallach International Conference on Machine Learning, 60-69, 2018 | 437 | 2018 |
| A reliable effective terascale linear learning system A Agarwal, O Chapelle, M Dudík, J Langford The Journal of Machine Learning Research 15 (1), 1111-1133, 2014 | 401 | 2014 |
| Fast global convergence of gradient methods for high-dimensional statistical recovery A Agarwal, S Negahban, MJ Wainwright The Annals of Statistics, 2452-2482, 2012 | 361* | 2012 |
| Taming the monster: A fast and simple algorithm for contextual bandits A Agarwal, D Hsu, S Kale, J Langford, L Li, R Schapire International Conference on Machine Learning, 1638-1646, 2014 | 351 | 2014 |
| Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback. A Agarwal, O Dekel, L Xiao COLT, 28-40, 2010 | 288 | 2010 |
| Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions A Agarwal, S Negahban, MJ Wainwright The Annals of Statistics, 1171-1197, 2012 | 265 | 2012 |
| Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization A Agarwal, PL Bartlett, P Ravikumar, MJ Wainwright IEEE Transactions on Information Theory 58 (5), 3235-3249, 2012 | 238 | 2012 |
| Learning to search better than your teacher KW Chang, A Krishnamurthy, A Agarwal, H Daume, J Langford International Conference on Machine Learning, 2058-2066, 2015 | 184 | 2015 |
| Contextual decision processes with low Bellman rank are PAC-learnable N Jiang, A Krishnamurthy, A Agarwal, J Langford, RE Schapire International Conference on Machine Learning, 1704-1713, 2017 | 180 | 2017 |
| Optimality and approximation with policy gradient methods in markov decision processes A Agarwal, SM Kakade, JD Lee, G Mahajan Conference on Learning Theory, 64-66, 2020 | 167 | 2020 |
| 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 | 162 | 2016 |
| Information-theoretic lower bounds on the oracle complexity of convex optimization A Agarwal, MJ Wainwright, P Bartlett, P Ravikumar Advances in Neural Information Processing Systems 22, 1-9, 2009 | 134 | 2009 |
| Learning to rank networked entities A Agarwal, S Chakrabarti, S Aggarwal Proceedings of the 12th ACM SIGKDD international conference on Knowledge …, 2006 | 134 | 2006 |
| Fast convergence of regularized learning in games V Syrgkanis, A Agarwal, H Luo, RE Schapire arXiv preprint arXiv:1507.00407, 2015 | 128 | 2015 |
| A lower bound for the optimization of finite sums A Agarwal, L Bottou International conference on machine learning, 78-86, 2015 | 128 | 2015 |
| Deep batch active learning by diverse, uncertain gradient lower bounds JT Ash, C Zhang, A Krishnamurthy, J Langford, A Agarwal arXiv preprint arXiv:1906.03671, 2019 | 125 | 2019 |
| Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes. P Ravikumar, A Agarwal, MJ Wainwright Journal of Machine Learning Research 11 (3), 2010 | 123 | 2010 |
| Optimal and adaptive off-policy evaluation in contextual bandits YX Wang, A Agarwal, M Dudık International Conference on Machine Learning, 3589-3597, 2017 | 112 | 2017 |
John LangfordMicrosoft Research New YorkVerified email at hunch.net
