  For sponsored search auctions, we consider contextual multi-armed bandit
problem in the presence of strategic agents. In this setting, at each round, an
advertising platform (center) runs an auction to select the best-suited ads
relevant to the query posted by the user. It is in the best interest of the
center to select an ad that has a high expected value (i.e., probability of
getting a click $\times$ value it derives from a click of the ad). The
probability of getting a click (CTR) is unknown to the center and depends on
the user's profile (context) posting the query. Further, the value derived for
a click is the private information to the advertiser and thus needs to be
elicited truthfully. The existing solution in this setting is not practical as
it suffers from very high regret ($O(T^{\frac{2}{3}})$).
