| 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 | 1315 | 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 | 1004 | 2018 |
| Distributed delayed stochastic optimization A Agarwal, JC Duchi Advances in neural information processing systems 24, 2011 | 716 | 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 | 585* | 2021 |
| 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 | 556 | 2019 |
| 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 | 522 | 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 | 475 | 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 | 433 | 2014 |
| 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 | 416 | 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 | 400 | 2017 |
| Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback. A Agarwal, O Dekel, L Xiao Colt, 28-40, 2010 | 382 | 2010 |
| Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions A Agarwal, S Negahban, MJ Wainwright | 301 | 2012 |
| Fast convergence of regularized learning in games V Syrgkanis, A Agarwal, H Luo, RE Schapire Advances in Neural Information Processing Systems 28, 2015 | 237 | 2015 |
| Fair regression: Quantitative definitions and reduction-based algorithms A Agarwal, M Dudík, ZS Wu International Conference on Machine Learning, 120-129, 2019 | 222 | 2019 |
| 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 | 222 | 2019 |
| 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 | 218 | 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 | 207 | 2011 |
| Model-based rl in contextual decision processes: Pac bounds and exponential improvements over model-free approaches W Sun, N Jiang, A Krishnamurthy, A Agarwal, J Langford Conference on learning theory, 2898-2933, 2019 | 200 | 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 | 200 | 2017 |
| Optimal and adaptive off-policy evaluation in contextual bandits YX Wang, A Agarwal, M Dudık International Conference on Machine Learning, 3589-3597, 2017 | 199 | 2017 |
John LangfordMicrosoft Research New YorkZweryfikowany adres z hunch.net
