  Cutting plane methods are a fundamental approach for solving integer linear
programs (ILPs). In each iteration of such methods, additional linear
constraints (cuts) are introduced to the constraint set with the aim of
excluding the previous fractional optimal solution while not affecting the
optimal integer solution. In this work, we explore a novel approach within
cutting plane methods: instead of only adding new cuts, we also consider the
removal of previous cuts introduced at any of the preceding iterations of the
method under a learnable parametric criteria. We demonstrate that in
fundamental combinatorial optimization settings such cut removal policies can
lead to significant improvements over both human-based and machine
learning-guided cut addition policies even when implemented with simple models.
