| Making gradient descent optimal for strongly convex stochastic optimization A Rakhlin, O Shamir, K Sridharan International Conference on Machine Learning (ICML), 2011 | 469 | 2011 |
| Competing in the dark: An efficient algorithm for bandit linear optimization JD Abernethy, E Hazan, A Rakhlin | 281 | 2009 |
| Non-convex learning via stochastic gradient Langevin dynamics: a nonasymptotic analysis M Raginsky, A Rakhlin, M Telgarsky arXiv preprint arXiv:1702.03849, 2017 | 199 | 2017 |
| Size-independent sample complexity of neural networks N Golowich, A Rakhlin, O Shamir Conference On Learning Theory, 297-299, 2018 | 197 | 2018 |
| Adaptive online gradient descent PL Bartlett, E Hazan, A Rakhlin Advances in Neural Information Processing Systems, 65-72, 2007 | 192* | 2007 |
| Online learning with predictable sequences A Rakhlin, K Sridharan | 159 | 2013 |
| Optimization, learning, and games with predictable sequences S Rakhlin, K Sridharan Advances in Neural Information Processing Systems, 3066-3074, 2013 | 150 | 2013 |
| Online optimization: Competing with dynamic comparators A Jadbabaie, A Rakhlin, S Shahrampour, K Sridharan Artificial Intelligence and Statistics, 398-406, 2015 | 134 | 2015 |
| Optimal strategies and minimax lower bounds for online convex games J Abernethy, PL Bartlett, A Rakhlin, A Tewari | 129 | 2008 |
| Stability of -Means Clustering A Rakhlin, A Caponnetto Advances in neural information processing systems, 1121-1128, 2007 | 117 | 2007 |
| Stochastic convex optimization with bandit feedback A Agarwal, DP Foster, D Hsu, SM Kakade, A Rakhlin SIAM Journal on Optimization 23 (1), 213-240, 2013 | 115 | 2013 |
| Online learning: Random averages, combinatorial parameters, and learnability A Rakhlin, K Sridharan, A Tewari Advances in Neural Information Processing Systems, 1984-1992, 2010 | 112* | 2010 |
| Just interpolate: Kernel “ridgeless” regression can generalize T Liang, A Rakhlin Annals of Statistics 48 (3), 1329-1347, 2020 | 104 | 2020 |
| Fisher-rao metric, geometry, and complexity of neural networks T Liang, T Poggio, A Rakhlin, J Stokes The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 99 | 2019 |
| A stochastic view of optimal regret through minimax duality J Abernethy, A Agarwal, PL Bartlett, A Rakhlin Conference on Learning Theory, 2009 | 91 | 2009 |
| Partial monitoring—classification, regret bounds, and algorithms G Bartók, DP Foster, D Pál, A Rakhlin, C Szepesvári Mathematics of Operations Research 39 (4), 967-997, 2014 | 89 | 2014 |
| Does data interpolation contradict statistical optimality? M Belkin, A Rakhlin, AB Tsybakov The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 83 | 2019 |
| High-probability regret bounds for bandit online linear optimization PL Bartlett, V Dani, T Hayes, S Kakade, A Rakhlin, A Tewari Conference on Learning Theory, 2008 | 81 | 2008 |
| Online learning: Beyond regret A Rakhlin, K Sridharan, A Tewari | 73 | 2011 |
| Distributed detection: Finite-time analysis and impact of network topology S Shahrampour, A Rakhlin, A Jadbabaie IEEE Transactions on Automatic Control 61 (11), 3256-3268, 2015 | 72 | 2015 |
