| Making gradient descent optimal for strongly convex stochastic optimization A Rakhlin, O Shamir, K Sridharan International Conference on Machine Learning (ICML), 2011 | 936 | 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 | 707 | 2017 |
| Size-independent sample complexity of neural networks N Golowich, A Rakhlin, O Shamir Conference On Learning Theory, 297-299, 2018 | 588 | 2018 |
| Deep learning: a statistical viewpoint PL Bartlett, A Montanari, A Rakhlin Acta numerica 30, 87-201, 2021 | 493 | 2021 |
| Optimization, learning, and games with predictable sequences S Rakhlin, K Sridharan Advances in Neural Information Processing Systems 26, 2013 | 488 | 2013 |
| Competing in the dark: An efficient algorithm for bandit linear optimization JD Abernethy, E Hazan, A Rakhlin Conference on Learning Theory, 2009 | 483 | 2009 |
| Online learning with predictable sequences A Rakhlin, K Sridharan Conference on Learning Theory, 993-1019, 2013 | 468 | 2013 |
| Just interpolate T Liang, A Rakhlin The Annals of Statistics 48 (3), 1329-1347, 2020 | 457 | 2020 |
| Online optimization: Competing with dynamic comparators A Jadbabaie, A Rakhlin, S Shahrampour, K Sridharan Artificial Intelligence and Statistics, 398-406, 2015 | 361 | 2015 |
| Adaptive online gradient descent PL Bartlett, E Hazan, A Rakhlin Advances in Neural Information Processing Systems, 65-72, 2007 | 313 | 2007 |
| Beyond ucb: Optimal and efficient contextual bandits with regression oracles D Foster, A Rakhlin International conference on machine learning, 3199-3210, 2020 | 302 | 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 | 294 | 2019 |
| Does data interpolation contradict statistical optimality? M Belkin, A Rakhlin, AB Tsybakov The 22nd international conference on artificial intelligence and statistics …, 2019 | 290 | 2019 |
| The statistical complexity of interactive decision making DJ Foster, SM Kakade, J Qian, A Rakhlin arXiv preprint arXiv:2112.13487, 2021 | 286 | 2021 |
| Stochastic convex optimization with bandit feedback A Agarwal, DP Foster, DJ Hsu, SM Kakade, A Rakhlin Advances in Neural Information Processing Systems 24, 2011 | 248 | 2011 |
| Near optimal finite time identification of arbitrary linear dynamical systems T Sarkar, A Rakhlin International Conference on Machine Learning, 5610-5618, 2019 | 247 | 2019 |
| Optimal strategies and minimax lower bounds for online convex games J Abernethy, PL Bartlett, A Rakhlin, A Tewari | 221 | 2008 |
| Finite time LTI system identification T Sarkar, A Rakhlin, MA Dahleh Journal of Machine Learning Research 22 (26), 1-61, 2021 | 198 | 2021 |
| Size-independent sample complexity of neural networks N Golowich, A Rakhlin, O Shamir Information and Inference: A Journal of the IMA 9 (2), 473-504, 2020 | 188 | 2020 |
| On the multiple descent of minimum-norm interpolants and restricted lower isometry of kernels T Liang, A Rakhlin, X Zhai Conference on Learning Theory, 2683-2711, 2020 | 175* | 2020 |
