Making gradient descent optimal for strongly convex stochastic optimization A Rakhlin, O Shamir, K Sridharan
International Conference on Machine Learning (ICML), 2011
600 2011 Size-independent sample complexity of neural networks N Golowich, A Rakhlin, O Shamir
Conference On Learning Theory, 297-299, 2018
345 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
344 2017 Competing in the dark: An efficient algorithm for bandit linear optimization JD Abernethy, E Hazan, A Rakhlin
334 2009 Optimization, learning, and games with predictable sequences S Rakhlin, K Sridharan
Advances in Neural Information Processing Systems 26, 2013
244 2013 Online learning with predictable sequences A Rakhlin, K Sridharan
Conference on Learning Theory, 993-1019, 2013
229 2013 Just interpolate: Kernel “ridgeless” regression can generalize T Liang, A Rakhlin
The Annals of Statistics 48 (3), 1329-1347, 2020
223 2020 Adaptive online gradient descent PL Bartlett, E Hazan, A Rakhlin
Advances in Neural Information Processing Systems, 65-72, 2007
216 * 2007 Online optimization: Competing with dynamic comparators A Jadbabaie, A Rakhlin, S Shahrampour, K Sridharan
Artificial Intelligence and Statistics, 398-406, 2015
193 2015 Stochastic convex optimization with bandit feedback A Agarwal, DP Foster, DJ Hsu, SM Kakade, A Rakhlin
Advances in Neural Information Processing Systems 24, 2011
166 2011 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
161 2019 Does data interpolation contradict statistical optimality? M Belkin, A Rakhlin, AB Tsybakov
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
152 2019 Optimal strategies and minimax lower bounds for online convex games J Abernethy, PL Bartlett, A Rakhlin, A Tewari
152 2008 Stability of A Rakhlin, A Caponnetto
Advances in neural information processing systems 19, 2006
133 2006 Online learning: Random averages, combinatorial parameters, and learnability A Rakhlin, K Sridharan, A Tewari
Advances in Neural Information Processing Systems 23, 2010
112 2010 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
110 2014 Near optimal finite time identification of arbitrary linear dynamical systems T Sarkar, A Rakhlin
International Conference on Machine Learning, 5610-5618, 2019
104 2019 A stochastic view of optimal regret through minimax duality J Abernethy, A Agarwal, PL Bartlett, A Rakhlin
Conference on Learning Theory, 2009
100 2009 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
100 2008 Theory of deep learning IIb: Optimization properties of SGD C Zhang, Q Liao, A Rakhlin, B Miranda, N Golowich, T Poggio
arXiv preprint arXiv:1801.02254, 2018
99 2018 