Alexander Rakhlin
Alexander Rakhlin
Associate Professor, MIT
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Making gradient descent optimal for strongly convex stochastic optimization
A Rakhlin, O Shamir, K Sridharan
International Conference on Machine Learning (ICML), 2011
Competing in the dark: An efficient algorithm for bandit linear optimization
JD Abernethy, E Hazan, A Rakhlin
Non-convex learning via stochastic gradient Langevin dynamics: a nonasymptotic analysis
M Raginsky, A Rakhlin, M Telgarsky
arXiv preprint arXiv:1702.03849, 2017
Size-independent sample complexity of neural networks
N Golowich, A Rakhlin, O Shamir
Conference On Learning Theory, 297-299, 2018
Adaptive online gradient descent
PL Bartlett, E Hazan, A Rakhlin
Advances in Neural Information Processing Systems, 65-72, 2007
Online learning with predictable sequences
A Rakhlin, K Sridharan
Optimization, learning, and games with predictable sequences
S Rakhlin, K Sridharan
Advances in Neural Information Processing Systems 26, 3066-3074, 2013
Optimal strategies and minimax lower bounds for online convex games
J Abernethy, PL Bartlett, A Rakhlin, A Tewari
Online optimization: Competing with dynamic comparators
A Jadbabaie, A Rakhlin, S Shahrampour, K Sridharan
Artificial Intelligence and Statistics, 398-406, 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
Stability of -Means Clustering
A Rakhlin, A Caponnetto
Advances in neural information processing systems, 1121-1128, 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
Just interpolate: Kernel “ridgeless” regression can generalize
T Liang, A Rakhlin
Annals of Statistics 48 (3), 1329-1347, 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
A stochastic view of optimal regret through minimax duality
J Abernethy, A Agarwal, PL Bartlett, A Rakhlin
Conference on Learning Theory, 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
Does data interpolation contradict statistical optimality?
M Belkin, A Rakhlin, AB Tsybakov
The 22nd International Conference on Artificial Intelligence and Statistics …, 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
Online learning: Beyond regret
A Rakhlin, K Sridharan, A Tewari
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
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