Instance-Dependent Regret Bounds for Nonstochastic Linear Partial Monitoring

In contrast to the classic formulation of partial monitoring, linear partial monitoring
can model infinite outcome spaces, while imposing a linear structure on both the
losses and the observations. This setting can be viewed as a generalization of
linear bandits where loss and feedback are decoupled in a flexible manner. In
this work, we address a nonstochastic (adversarial), finite-actions version of the
problem through a simple instance of the exploration-by-optimization method that
is amenable to efficient implementation. We derive regret bounds that depend
on the game structure in a more transparent manner than previous theoretical
guarantees for this paradigm. Our bounds feature instance-specific quantities that
reflect the degree of alignment between observations and losses, and resemble
known guarantees in the stochastic setting. Notably, they achieve the standard√T rate in easy (locally observable) games and T 2/3 in hard (globally observable)
games, where T is the time horizon. We instantiate these bounds in a selection of
old and new partial information settings subsumed by this model, and illustrate that
the achieved dependence on the game structure can be tight in interesting cases.