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We consider the stochastic contextual bandit problem under the high dimensional linear model.
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Sparse online learning via truncated gradient
J. Langford, L. Li, and T. Zhang · 2009
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Sharp thresholds for high-dimensional and noisy sparsity recovery using ℓ 1 \ell_{1} -constrained quadratic programming (Lasso)
M. J. Wainwright · 2009
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Simultaneous analysis of lasso and dantzig selector
P. J. Bickel, Y. Ritov, and A. B. Tsybakov · 2009
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Iterative hard thresholding for compressed sensing
T. Blumensath and M. E. Davies · 2009
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Sparsity regret bounds for individual sequences in online linear regression
S. Gerchinovitz · 2013
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A linear response bandit problem
A. Goldenshluger and A. Zeevi · 2013
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On the complexity of bandit and derivative-free stochastic convex optimization
O. Shamir · 2013
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Sparsity regret bounds for individual sequences in online linear regression
S. Gerchinovitz · 2013
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A linear response bandit problem
A. Goldenshluger and A. Zeevi · 2013
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On the complexity of bandit and derivative-free stochastic convex optimization
O. Shamir · 2013
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Sparse online learning via truncated gradient
J. Langford, L. Li, and T. Zhang · 2009
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Sharp thresholds for high-dimensional and noisy sparsity recovery using ℓ 1 \ell_{1} -constrained quadratic programming (Lasso)
M. J. Wainwright · 2009
Cited alongside, same era.
Optimal algorithms for online convex optimization with multi-point bandit feedback
A. Agarwal, O. Dekel, and L. Xiao · 2010
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Inference for non-regular parameters in optimal dynamic treatment regimes
B. Chakraborty, S. Murphy, and V. Strecher · 2010
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Parametric bandits: The generalized linear case
S. Filippi, O. Cappe, A. Garivier, and C. Szepesvári · 2010
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A contextual-bandit approach to personalized news article recommendation
L. Li, W. Chu, J. Langford, and R. E. Schapire · 2010
Cited alongside, same era.
Taming the monster: A fast and simple algorithm for contextual bandits
A. Agarwal, D. Hsu, S. Kale, J. Langford, L. Li, and R. Schapire · 2014
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Doubly robust learning for estimating individualized treatment with censored data
Y.-Q. Zhao, D. Zeng, E. B. Laber, R. Song, M. Yuan, and M. R. Kosorok · 2014
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Taming the monster: A fast and simple algorithm for contextual bandits
A. Agarwal, D. Hsu, S. Kale, J. Langford, L. Li, and R. Schapire · 2014
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Doubly robust learning for estimating individualized treatment with censored data
Y.-Q. Zhao, D. Zeng, E. B. Laber, R. Song, M. Yuan, and M. R. Kosorok · 2014
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Online decision-making with high-dimensional covariates
H. Bastani and M. Bayati · 2015
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Non-stationary stochastic optimization
O. Besbes, Y. Gur, and A. Zeevi · 2015
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Linear multi-resource allocation with semi-bandit feedback
T. Lattimore, K. Crammer, and C. Szepesvári · 2015
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Sparse learning via boolean relaxations
M. Pilanci, M. J. Wainwright, and L. El Ghaoui · 2015
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On the complexity of bandit linear optimization
O. Shamir · 2015
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Penalized Q-learning for dynamic treatment regimens
R. Song, W. Wang, D. Zeng, and M. R. Kosorok · 2015
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Online decision-making with high-dimensional covariates
H. Bastani and M. Bayati · 2015
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Non-stationary stochastic optimization
O. Besbes, Y. Gur, and A. Zeevi · 2015
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Linear multi-resource allocation with semi-bandit feedback
T. Lattimore, K. Crammer, and C. Szepesvári · 2015
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Sparse learning via boolean relaxations
M. Pilanci, M. J. Wainwright, and L. El Ghaoui · 2015
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On the complexity of bandit linear optimization
O. Shamir · 2015
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Penalized Q-learning for dynamic treatment regimens
R. Song, W. Wang, D. Zeng, and M. R. Kosorok · 2015
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Best subset selection via a modern optimization lens
D. Bertsimas, A. King, and R. Mazumder · 2016
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Online sparse linear regression
D. Foster, S. Kale, and H. Karloff · 2016
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Best subset selection via a modern optimization lens
D. Bertsimas, A. King, and R. Mazumder · 2016
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Online sparse linear regression
D. Foster, S. Kale, and H. Karloff · 2016
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Kernel-based methods for bandit convex optimization
S. Bubeck, Y. T. Lee, and R. Eldan · 2017
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Provably optimal algorithms for generalized linear contextual bandits
L. Li, Y. Lu, and D. Zhou · 2017
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Stochastic zeroth-order optimization in high dimensions
Y. Wang, S. Du, S. Balakrishnan, and A. Singh · 2017
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Kernel-based methods for bandit convex optimization
S. Bubeck, Y. T. Lee, and R. Eldan · 2017
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Provably optimal algorithms for generalized linear contextual bandits
L. Li, Y. Lu, and D. Zhou · 2017
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Stochastic zeroth-order optimization in high dimensions
Y. Wang, S. Du, S. Balakrishnan, and A. Singh · 2017
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Zeroth-order (non)-convex stochastic optimization via conditional gradient and gradient updates
K. Balasubramanian and S. Ghadimi · 2018
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Practical contextual bandits with regression oracles
D. J. Foster, A. Agarwal, M. Dudík, H. Luo, and R. E. Schapire · 2018
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Lower bounds for stochastic linear bandits
C. Szepesvari · 2018
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Zeroth-order (non)-convex stochastic optimization via conditional gradient and gradient updates
K. Balasubramanian and S. Ghadimi · 2018
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Practical contextual bandits with regression oracles
D. J. Foster, A. Agarwal, M. Dudík, H. Luo, and R. E. Schapire · 2018
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Lower bounds for stochastic linear bandits
C. Szepesvari · 2018
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Contextual learning with online convex optimization: Theory and application to chronic diseases
E. Keyvanshokooh, M. Zhalechian, C. Shi, M. P. Van Oyen, and P. Kazemian · 2019
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Contextual learning with online convex optimization: Theory and application to chronic diseases
E. Keyvanshokooh, M. Zhalechian, C. Shi, M. P. Van Oyen, and P. Kazemian · 2019
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