  While machine-learning models are flourishing and transforming many aspects
of everyday life, the inability of humans to understand complex models poses
difficulties for these models to be fully trusted and embraced. Thus,
interpretability of models has been recognized as an equally important quality
as their predictive power. In particular, rule-based systems are experiencing a
renaissance owing to their intuitive if-then representation.
  However, simply being rule-based does not ensure interpretability. For
example, overlapped rules spawn ambiguity and hinder interpretation. Here we
propose a novel approach of inferring diverse rule sets, by optimizing small
overlap among decision rules with a 2-approximation guarantee under the
framework of Max-Sum diversification. We formulate the problem as maximizing a
weighted sum of discriminative quality and diversity of a rule set.
  In order to overcome an exponential-size search space of association rules,
we investigate several natural options for a small candidate set of
high-quality rules, including frequent and accurate rules, and examine their
hardness. Leveraging the special structure in our formulation, we then devise
an efficient randomized algorithm, which samples rules that are highly
discriminative and have small overlap. The proposed sampling algorithm
analytically targets a distribution of rules that is tailored to our objective.
  We demonstrate the superior predictive power and interpretability of our
model with a comprehensive empirical study against strong baselines.
