Simulation Studies of Multi-armed Bandits with Covariates (Invited Paper)

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A contextual-bandit approach to personalized news article recommendation (2010, background): “Algorithm 1 (or the similar algorithm in [22]) computes the inverse of the matrix, D a Da + Id (or D a Da), where Da is again the design matrix with rows corresponding to features in the training data.”
Optimistic Bayesian Sampling in Contextual-Bandit Problems (2012, methodology): “For contextual bandit problems, Interval estimation (IE) methods, such as those suggested by Kaelbling (1994), Pavlidis et al. (2008) and Li et al. (2010) (under the name LinUCB), have become popular.”
Contextual Bandits with Linear Payoff Functions (2011, methodology): “1 While LinUCB has been experimentally tested [Li et al., 2010, Pavlidis et al., 2008] , no theoretical analysis has been carried out.”
On Bayesian Upper Confidence Bounds for Bandit Problems (2012, methodology): “The use of fixed-level quantiles of (λj)1≤j≤K as confidence indices appears in [14] as a special case of the Interval Estimation method.”