… Working towards an affirmative answer to this question, we consider a semiparametric
contextual bandit setup where the reward is modeled as a linear function of the decision …

… tures in semi-parametric bandits. It is the first time to propose a novel Semi-Parametric
Contextual Bandits Problem, as far as we known, for cases where contextual features are evolved …

… the seller can leverage contextual information describing the … Unlike in usual contextual
bandit settings, the optimal price/… We develop a semi-parametric model in which the residual …

… We study the semi-parametric contextual bandit problem for multiple users equipped with a
user network. Suppose that there are n users, say j ∈ V = { 1 , … , n } . For each time step t = …

… We study the semi-parametric contextual bandit problem for multiple users equipped with
a user network. Suppose that there are n users, say j ∈ V = {1,...,n}. For each time step t = 1,...,T…

… Contextual bandit algorithms were then proposed to improve the efficiency when the arm …
contextual bandit algorithms may suffer linear regret bound in more general semi-parametric …

… the context of the action. This paper proposes a new contextual MAB algorithm for a relaxed,
semiparametric … We verify that the contextual bandit algorithms achieve substantially higher …

… review existing contextual bandit algorithms and their theoretical properties. In Chapter 3,
we present a new contextual MAB algorithm which works well under a semiparametric reward …

… strong practical advantage of balanced contextual bandits on a large … Additionally, we develop
contextual bandits with simpler … Adjusting for nonignorable drop-out using semiparametric …

… Learning algorithms for contextual bandits include theoretical … A recent paper about empirical
contextual bandit learning [4] … Our combination of CIs with learning is a contextual bandit …