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We consider the problem of bandit optimization, inspired by stochastic optimization and online learning problems with bandit feedback.
An algorithm for quadratic programming
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Optimal order of accuracy of search algorithms in stochastic optimization
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Microeconomic theory
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The non-stochastic multi-armed bandit problem
P. Auer, N. Cesa-Bianchi, Y. Freund, and R. Schapire · 2002
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Accelerated randomized stochastic optimization
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Introductory Lectures on Convex Optimization
Yuri Nesterov · 2003
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Prediction, Learning, and Games
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Regret minimization under partial monitoring
Nicolò Cesa-Bianchi, Gábor Lugosi, and Gilles Stoltz · 2006
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Logarithmic regret algorithms for online convex optimization
Elad Hazan, Amit Agarwal, and Satyen Kale · 2007
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Minimax policies for adversarial and stochastic bandits
Jean-Yves Audibert and Sébastien Bubeck · 2009
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Online learning for global cost functions
E. Even-Dar, R. Kleinberg, S. Mannor, and Y. Mansour · 2009
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Stochastic convex optimization with bandit feedback
Alekh Agarwal, Dean P. Foster, Daniel Hsu, Sham M. Kakade, and Alexander Rakhlin · 2011
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Sparse Convex Optimization Methods for Machine Learning
Martin Jaggi · 2011
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Online learning: Beyond regret
Alexander Rakhlin, Karthik Sridharan, and Ambuj Tewari · 2011
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Online learning and online convex optimization
Shai Shalev-Shwartz · 2011
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Improved regret guarantees for online smooth convex optimization with bandit feedback
A. Saha and A. Tewari · 2011
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Regret analysis of stochastic and nonstochastic multi-armed bandit problems
Sébastien Bubeck and Nicolo Cesa-Bianchi · 2012
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The convex optimization approach to regret minimization
Elad Hazan · 2012
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Logistic regression: Tight bounds for stochastic and online optimization
E. Hazan, T. Koren, and K. Levy · 2014
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Bandit convex optimization: Towards tight bounds
E. Hazan and K. Levy · 2014
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Approachability in unknown games: Online learning meets multi-objective optimization
Shie Mannor, Vianney Perchet, and Gilles Stoltz · 2014
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Upper-confidence-bound algorithms for active learning in multi-armed bandits
Alexandra Carpentier, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos, and András Antos · 2015
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On the online frank-wolfe algorithms for convex and non-convex optimizations
Jean Lafond, Hoi-To Wai, and Eric Moulines · 2015
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An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives
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Query complexity of derivative-free optimization
Kevin G Jamieson, Robert Nowak, and Ben Recht · 2012
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Non-strongly-convex smooth stochastic approximation with convergence rate o ( 1 / n ) o(1/n)
F. Bach and E. Moulines · 2013
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An affine invariant linear convergence analysis for frank-wolfe algorithms
Simon Lacoste-Julien and Martin Jaggi · 2013
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The multi-armed bandit problem with covariates
Vianney Perchet and Philippe Rigollet · 2013
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On the complexity of bandit and derivative-free stochastic convex optimization
O. Shamir · 2013
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Bandits with concave rewards and convex knapsacks
Shipra Agrawal and Nikhil R. Devanur · 2014
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Shipra Agrawal, Nikhil R. Devanur, and Lihong Li · 2016
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Resource allocation for statistical estimation
Quentin Berthet and Venkat Chandrasekaran · 2016
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Kernel-based methods for bandit convex optimization
Sébastien Bubeck, Ronen Eldan, and Yin Tat Lee · 2016
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Highly-smooth zero-th order online optimization
Francis Bach and Vianney Perchet · 2016
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Local asymptotics for some stochastic optimization problems: Optimality, constraint identification, and dual averaging
John Duchi and Feng Ruan · 2016
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Batched bandit problems
Vianney Perchet, Philippe Rigollet, Sylvain Chassang, and Erik Snowberg · 2016
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