  We propose a new online algorithm for cumulative regret minimization in a
stochastic linear bandit. The algorithm pulls the arm with the highest
estimated reward in a linear model trained on its perturbed history. Therefore,
we call it perturbed-history exploration in a linear bandit (LinPHE). The
perturbed history is a mixture of observed rewards and randomly generated
i.i.d. pseudo-rewards. We derive a $\tilde{O}(d \sqrt{n})$ gap-free bound on
the $n$-round regret of LinPHE, where $d$ is the number of features. The key
steps in our analysis are new concentration and anti-concentration bounds on
the weighted sum of Bernoulli random variables. To show the generality of our
design, we generalize LinPHE to a logistic model. We evaluate our algorithms
empirically and show that they are practical.

Machine Learning;Machine Learning