  In the stochastic contextual low-rank matrix bandit problem, the expected
reward of an action is given by the inner product between the action's feature
matrix and some fixed, but initially unknown $d_1$ by $d_2$ matrix $\Theta^*$
with rank $r \ll \{d_1, d_2\}$, and an agent sequentially takes actions based
on past experience to maximize the cumulative reward. In this paper, we study
the generalized low-rank matrix bandit problem, which has been recently
proposed in \cite{lu2021low} under the Generalized Linear Model (GLM)
framework. To overcome the computational infeasibility and theoretical restrain
of existing algorithms on this problem, we first propose the G-ESTT framework
that modifies the idea from \cite{jun2019bilinear} by using Stein's method on
the subspace estimation and then leverage the estimated subspaces via a
regularization idea. Furthermore, we remarkably improve the efficiency of
G-ESTT by using a novel exclusion idea on the estimated subspace instead, and
propose the G-ESTS framework. We also show that G-ESTT can achieve the
$\tilde{O}(\sqrt{(d_1+d_2)MrT})$ bound of regret while G-ESTS can achineve the
$\tilde{O}(\sqrt{(d_1+d_2)^{3/2}Mr^{3/2}T})$ bound of regret under mild
assumption up to logarithm terms, where $M$ is some problem dependent value.
Under a reasonable assumption that $M = O((d_1+d_2)^2)$ in our problem setting,
the regret of G-ESTT is consistent with the current best regret of
$\tilde{O}((d_1+d_2)^{3/2} \sqrt{rT}/D_{rr})$~\citep{lu2021low} ($D_{rr}$ will
be defined later). For completeness, we conduct experiments to illustrate that
our proposed algorithms, especially G-ESTS, are also computationally tractable
and consistently outperform other state-of-the-art (generalized) linear matrix
bandit methods based on a suite of simulations.
