| Making gradient descent optimal for strongly convex stochastic optimization A Rakhlin, O Shamir, K Sridharan International Conference on Machine Learning (ICML), 2011 | 678 | 2011 |
| Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis M Raginsky, A Rakhlin, M Telgarsky Conference on Learning Theory, 1674-1703, 2017 | 442 | 2017 |
| Size-independent sample complexity of neural networks N Golowich, A Rakhlin, O Shamir Conference On Learning Theory, 297-299, 2018 | 441 | 2018 |
| Competing in the dark: An efficient algorithm for bandit linear optimization JD Abernethy, E Hazan, A Rakhlin | 368 | 2009 |
| Optimization, learning, and games with predictable sequences S Rakhlin, K Sridharan Advances in Neural Information Processing Systems 26, 2013 | 311 | 2013 |
| Online learning with predictable sequences A Rakhlin, K Sridharan Conference on Learning Theory, 993-1019, 2013 | 286 | 2013 |
| Just interpolate: Kernel “ridgeless” regression can generalize T Liang, A Rakhlin | 283 | 2020 |
| Online optimization: Competing with dynamic comparators A Jadbabaie, A Rakhlin, S Shahrampour, K Sridharan Artificial Intelligence and Statistics, 398-406, 2015 | 239 | 2015 |
| Adaptive online gradient descent PL Bartlett, E Hazan, A Rakhlin Advances in Neural Information Processing Systems, 65-72, 2007 | 239* | 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 | 199 | 2019 |
| 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 |
| Does data interpolation contradict statistical optimality? M Belkin, A Rakhlin, AB Tsybakov The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 187 | 2019 |
| Optimal strategies and minimax lower bounds for online convex games J Abernethy, PL Bartlett, A Rakhlin, A Tewari | 171 | 2008 |
| Deep learning: a statistical viewpoint PL Bartlett, A Montanari, A Rakhlin Acta numerica 30, 87-201, 2021 | 156 | 2021 |
| Near optimal finite time identification of arbitrary linear dynamical systems T Sarkar, A Rakhlin International Conference on Machine Learning, 5610-5618, 2019 | 151 | 2019 |
| Stability of -Means Clustering A Rakhlin, A Caponnetto Advances in neural information processing systems 19, 2006 | 137 | 2006 |
| Online learning: Random averages, combinatorial parameters, and learnability A Rakhlin, K Sridharan, A Tewari Advances in Neural Information Processing Systems 23, 2010 | 128 | 2010 |
| Beyond ucb: Optimal and efficient contextual bandits with regression oracles D Foster, A Rakhlin International Conference on Machine Learning, 3199-3210, 2020 | 123 | 2020 |
| 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 | 122 | 2014 |
| 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 | 114 | 2008 |
