| Making gradient descent optimal for strongly convex stochastic optimization A Rakhlin, O Shamir, K Sridharan International Conference on Machine Learning (ICML), 2011 | 502 | 2011 |
| Competing in the dark: An efficient algorithm for bandit linear optimization JD Abernethy, E Hazan, A Rakhlin | 304 | 2009 |
| Size-independent sample complexity of neural networks N Golowich, A Rakhlin, O Shamir Conference On Learning Theory, 297-299, 2018 | 241 | 2018 |
| Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis M Raginsky, A Rakhlin, M Telgarsky Conference on Learning Theory, 1674-1703, 2017 | 231 | 2017 |
| Adaptive online gradient descent PL Bartlett, E Hazan, A Rakhlin Advances in Neural Information Processing Systems, 65-72, 2007 | 203 | 2007 |
| Online learning with predictable sequences A Rakhlin, K Sridharan Conference on Learning Theory, 993-1019, 2013 | 180 | 2013 |
| Optimization, learning, and games with predictable sequences A Rakhlin, K Sridharan arXiv preprint arXiv:1311.1869, 2013 | 174 | 2013 |
| Optimal strategies and minimax lower bounds for online convex games J Abernethy, PL Bartlett, A Rakhlin, A Tewari | 150 | 2008 |
| Online optimization: Competing with dynamic comparators A Jadbabaie, A Rakhlin, S Shahrampour, K Sridharan Artificial Intelligence and Statistics, 398-406, 2015 | 143 | 2015 |
| Just interpolate: Kernel “ridgeless” regression can generalize T Liang, A Rakhlin Annals of Statistics 48 (3), 1329-1347, 2020 | 134 | 2020 |
| Stability of k-means clustering A Rakhlin, A Caponnetto Advances in neural information processing systems 19, 1121, 2007 | 127 | 2007 |
| 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 | 118 | 2019 |
| A stochastic view of optimal regret through minimax duality J Abernethy, A Agarwal, PL Bartlett, A Rakhlin Conference on Learning Theory, 2009 | 102 | 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 | 100 | 2014 |
| Online learning: Random averages, combinatorial parameters, and learnability A Rakhlin, K Sridharan, A Tewari | 99 | 2010 |
| Does data interpolation contradict statistical optimality? M Belkin, A Rakhlin, AB Tsybakov The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 97 | 2019 |
| Stochastic convex optimization with bandit feedback A Agarwal, DP Foster, D Hsu, SM Kakade, A Rakhlin arXiv preprint arXiv:1107.1744, 2011 | 93 | 2011 |
| 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 | 88 | 2008 |
| 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 | 79 | 2015 |
| Online learning: Beyond regret A Rakhlin, K Sridharan, A Tewari Proceedings of the 24th Annual Conference on Learning Theory, 559-594, 2011 | 75 | 2011 |
