Making gradient descent optimal for strongly convex stochastic optimization A Rakhlin, O Shamir, K Sridharan
International Conference on Machine Learning (ICML), 2011
481 2011 Competing in the dark: An efficient algorithm for bandit linear optimization JD Abernethy, E Hazan, A Rakhlin
294 2009 Non-convex learning via stochastic gradient Langevin dynamics: a nonasymptotic analysis M Raginsky, A Rakhlin, M Telgarsky
arXiv preprint arXiv:1702.03849, 2017
215 2017 Size-independent sample complexity of neural networks N Golowich, A Rakhlin, O Shamir
Conference On Learning Theory, 297-299, 2018
211 2018 Adaptive online gradient descent PL Bartlett, E Hazan, A Rakhlin
Advances in Neural Information Processing Systems, 65-72, 2007
200 * 2007 Online learning with predictable sequences A Rakhlin, K Sridharan
168 2013 Optimization, learning, and games with predictable sequences S Rakhlin, K Sridharan
Advances in Neural Information Processing Systems 26, 3066-3074, 2013
164 2013 Optimal strategies and minimax lower bounds for online convex games J Abernethy, PL Bartlett, A Rakhlin, A Tewari
143 2008 Online optimization: Competing with dynamic comparators A Jadbabaie, A Rakhlin, S Shahrampour, K Sridharan
Artificial Intelligence and Statistics, 398-406, 2015
136 2015 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
126 2013 Stability of A Rakhlin, A Caponnetto
Advances in neural information processing systems, 1121-1128, 2007
120 2007 Online learning: Random averages, combinatorial parameters, and learnability A Rakhlin, K Sridharan, A Tewari
Advances in neural information processing systems 23, 1984-1992, 2010
118 * 2010 Just interpolate: Kernel “ridgeless” regression can generalize T Liang, A Rakhlin
Annals of Statistics 48 (3), 1329-1347, 2020
115 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
107 2019 A stochastic view of optimal regret through minimax duality J Abernethy, A Agarwal, PL Bartlett, A Rakhlin
Conference on Learning Theory, 2009
97 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
92 2014 Does data interpolation contradict statistical optimality? M Belkin, A Rakhlin, AB Tsybakov
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
90 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
83 2008 Online learning: Beyond regret A Rakhlin, K Sridharan, A Tewari
77 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
74 2015 