  Mixed Integer Linear Programming (MILP) is a fundamental tool for modeling
combinatorial optimization problems. Recently, a growing body of research has
used machine learning to accelerate MILP solving. Despite the increasing
popularity of this approach, there is a lack of a common repository that
provides distributions of similar MILP instances across different domains, at
different hardness levels, with standardized test sets. In this paper, we
introduce Distributional MIPLIB, a multi-domain library of problem
distributions for advancing ML-guided MILP methods. We curate MILP
distributions from existing work in this area as well as real-world problems
that have not been used, and classify them into different hardness levels. It
will facilitate research in this area by enabling comprehensive evaluation on
diverse and realistic domains. We empirically illustrate the benefits of using
Distributional MIPLIB as a research vehicle in two ways. We evaluate the
performance of ML-guided variable branching on previously unused distributions
to identify potential areas for improvement. Moreover, we propose to learn
branching policies from a mix of distributions, demonstrating that mixed
distributions achieve better performance compared to homogeneous distributions
when there is limited data and generalize well to larger instances.
