# Is there a UCB type algorithm for linear stochastic bandit with lasso regression?

Why is there no upper confidence bound algorithm for linear stochastic bandits that uses lasso regression in the case that the regression parameters are sparse in the features?

In particular, I don't understand what is hard about lasso regression that makes it hard to be used in a UCB type algorithm whereas there is a lot of work on ridge regression based UCB algorithms see e.g. Yadkori et al.

I looked up some works e.g. Bastani and Bayati, Kim and Paik but they all do not a UCB-type algorithm, instead, they propose forced or probabilistic sampling to satisfy the compatibility condition (see Lemma EC.6. of Bastani and Bayati).