| 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 | 1425 | 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 | 1286 | 2018 |
| On the theory of policy gradient methods: Optimality, approximation, and distribution shift A Agarwal, SM Kakade, JD Lee, G Mahajan Journal of Machine Learning Research 22 (98), 1-76, 2021 | 841* | 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 | 835 | 2019 |
| Distributed delayed stochastic optimization A Agarwal, JC Duchi Advances in neural information processing systems 24, 2011 | 774 | 2011 |
| 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 | 592 | 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 | 521 | 2009 |
| 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 | 501 | 2017 |
| 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 | 449 | 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 | 444 | 2010 |
| Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback. A Agarwal, O Dekel, L Xiao Colt, 28-40, 2010 | 439 | 2010 |
| Fair regression: Quantitative definitions and reduction-based algorithms A Agarwal, M Dudík, ZS Wu International Conference on Machine Learning, 120-129, 2019 | 320 | 2019 |
| Reinforcement learning: Theory and algorithms A Agarwal, N Jiang, SM Kakade, W Sun CS Dept., UW Seattle, Seattle, WA, USA, Tech. Rep 32, 96, 2019 | 317 | 2019 |
| Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions A Agarwal, S Negahban, MJ Wainwright | 312 | 2012 |
| Flambe: Structural complexity and representation learning of low rank mdps A Agarwal, S Kakade, A Krishnamurthy, W Sun Advances in neural information processing systems 33, 20095-20107, 2020 | 292 | 2020 |
| Bellman-consistent pessimism for offline reinforcement learning T Xie, CA Cheng, N Jiang, P Mineiro, A Agarwal Advances in neural information processing systems 34, 6683-6694, 2021 | 291 | 2021 |
| Fast convergence of regularized learning in games V Syrgkanis, A Agarwal, H Luo, RE Schapire Advances in Neural Information Processing Systems 28, 2015 | 291 | 2015 |
| 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 | 279 | 2019 |
| 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 | 258 | 2019 |
| Hierarchical imitation and reinforcement learning H Le, N Jiang, A Agarwal, M Dudík, Y Yue, H Daumé III International conference on machine learning, 2917-2926, 2018 | 239 | 2018 |
John LangfordMicrosoft Research New YorkE-mail megerősítve itt: hunch.net
