  Directed information (DI) is a useful tool to explore time-directed
interactions in multivariate data. However, as originally formulated DI is not
well suited to interactions that change over time. In previous work, adaptive
directed information was introduced to accommodate non-stationarity, while
still preserving the utility of DI to discover complex dependencies between
entities. There are many design decisions and parameters that are crucial to
the effectiveness of ADI. Here, we apply ideas from ensemble learning in order
to alleviate this issue, allowing for a more robust estimator for exploratory
data analysis. We apply these techniques to interaction estimation in a crowded
scene, utilizing the Stanford drone dataset as an example.
