  This paper studies semiparametric contextual bandits, a generalization of the
linear stochastic bandit problem where the reward for an action is modeled as a
linear function of known action features confounded by an non-linear
action-independent term. We design new algorithms that achieve
$\tilde{O}(d\sqrt{T})$ regret over $T$ rounds, when the linear function is
$d$-dimensional, which matches the best known bounds for the simpler
unconfounded case and improves on a recent result of Greenewald et al. (2017).
Via an empirical evaluation, we show that our algorithms outperform prior
approaches when there are non-linear confounding effects on the rewards.
Technically, our algorithms use a new reward estimator inspired by
doubly-robust approaches and our proofs require new concentration inequalities
for self-normalized martingales.
