| 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 | 1266 | 2011 |
| A reductions approach to fair classification A Agarwal, A Beygelzimer, M Dudík, J Langford, H Wallach International Conference on Machine Learning, 60-69, 2018 | 853 | 2018 |
| Distributed delayed stochastic optimization A Agarwal, JC Duchi Advances in neural information processing systems 24, 2011 | 680 | 2011 |
| On the theory of policy gradient methods: Optimality, approximation, and distribution shift A Agarwal, SM Kakade, JD Lee, G Mahajan The Journal of Machine Learning Research 22 (1), 4431-4506, 2021 | 481* | 2021 |
| 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 | 479 | 2014 |
| 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, 2009 | 446 | 2009 |
| 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 | 427 | 2014 |
| 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 | 417 | 2019 |
| Fast global convergence rates of gradient methods for high-dimensional statistical recovery A Agarwal, S Negahban, MJ Wainwright Advances in Neural Information Processing Systems 23, 2010 | 406 | 2010 |
| Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback. A Agarwal, O Dekel, L Xiao Colt, 28-40, 2010 | 349 | 2010 |
| 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 | 343 | 2017 |
| Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions A Agarwal, S Negahban, MJ Wainwright | 294 | 2012 |
| Fast convergence of regularized learning in games V Syrgkanis, A Agarwal, H Luo, RE Schapire Advances in Neural Information Processing Systems 28, 2015 | 203 | 2015 |
| Learning to search better than your teacher KW Chang, A Krishnamurthy, A Agarwal, H Daumé III, J Langford International Conference on Machine Learning, 2058-2066, 2015 | 202 | 2015 |
| Stochastic convex optimization with bandit feedback A Agarwal, DP Foster, DJ Hsu, SM Kakade, A Rakhlin Advances in Neural Information Processing Systems 24, 2011 | 198 | 2011 |
| 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 | 182 | 2016 |
| Provably efficient rl with rich observations via latent state decoding S Du, A Krishnamurthy, N Jiang, A Agarwal, M Dudik, J Langford International Conference on Machine Learning, 1665-1674, 2019 | 179 | 2019 |
| Fair regression: Quantitative definitions and reduction-based algorithms A Agarwal, M Dudík, ZS Wu International Conference on Machine Learning, 120-129, 2019 | 178 | 2019 |
| Off-policy evaluation for slate recommendation A Swaminathan, A Krishnamurthy, A Agarwal, M Dudik, J Langford, ... Advances in Neural Information Processing Systems 30, 2017 | 178 | 2017 |
| Optimal and adaptive off-policy evaluation in contextual bandits YX Wang, A Agarwal, M Dudık International Conference on Machine Learning, 3589-3597, 2017 | 176 | 2017 |
John LangfordMicrosoft Research New YorkZweryfikowany adres z hunch.net
