  In many machine learning tasks, input features with varying degrees of
predictive capability are acquired at varying costs. In order to optimize the
performance-cost trade-off, one would select features to observe a priori.
However, given the changing context with previous observations, the subset of
predictive features to select may change dynamically. Therefore, we face the
challenging new problem of foresight dynamic selection (FDS): finding a dynamic
and light-weight policy to decide which features to observe next, before
actually observing them, for overall performance-cost trade-offs. To tackle
FDS, this paper proposes a Bayesian learning framework of Variational Foresight
Dynamic Selection (VFDS). VFDS learns a policy that selects the next feature
subset to observe, by optimizing a variational Bayesian objective that
characterizes the trade-off between model performance and feature cost. At its
core is an implicit variational distribution on binary gates that are dependent
on previous observations, which will select the next subset of features to
observe. We apply VFDS on the Human Activity Recognition (HAR) task where the
performance-cost trade-off is critical in its practice. Extensive results
demonstrate that VFDS selects different features under changing contexts,
notably saving sensory costs while maintaining or improving the HAR accuracy.
Moreover, the features that VFDS dynamically select are shown to be
interpretable and associated with the different activity types. We will release
the code.
