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Online recommendation/advertising is ubiquitous in web business.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Parametric bandits: The generalized linear case
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A contextual-bandit approach to personalized news article recommendation
L. Li, W. Chu, J. Langford, and R. E. Schapire · 2010
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Introduction to the non-asymptotic analysis of random matrices
R. Vershynin · 2010
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Identifying outlier arms in multi-armed bandit
H. Zhuang, C. Wang, and Y. Wang · 2010
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Improved algorithms for linear stochastic bandits
Y. Abbasi-Yadkori, D. Pál, and C. Szepesvári · 2011
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X-armed bandits
S. Bubeck, R. Munos, G. Stoltz, and C. Szepesvári · 2011
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G. Yang · 2019
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Generic outlier detection in multi-armed bandit
Y. Ban and J. He · 2020
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W. Zhang, D. Zhou, L. Li, and Q. Gu · 2020
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Neural contextual bandits with ucb-based exploration
D. Zhou, L. Li, and Q. Gu · 2020
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Generalization bounds of stochastic gradient descent for wide and deep neural networks
Y. Cao and Q. Gu · 2019
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Collaborative filtering bandits
S. Li, A. Karatzoglou, and C. Gentile
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Contextual combinatorial cascading bandits
S. Li, B. Wang, S. Zhang, and W. Chen
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Finite-time analysis of kernelised contextual bandits
M. Valko, N. Korda, R. Munos, I. Flaounas, and N. Cristianini
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Finite-time analysis of kernelised contextual bandits
M. Valko, N. Korda, R. Munos, I. Flaounas, and N. Cristianini
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Y. Ban and J. He · 2021
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Multi-facet contextual bandits: A neural network perspective
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A hybrid bandit model with visual priors for creative ranking in display advertising
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