Piecewise polynomial scoring policies for branch-and-cut, including ReLU networks, yield piecewise constant cost functions with pseudo-dimension bounds that imply sample complexity guarantees.
A machine learning-based approximation of strong branching
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Generalization Guarantees for Learning Branch-and-Cut Policies in Integer Programming
Piecewise polynomial scoring policies for branch-and-cut, including ReLU networks, yield piecewise constant cost functions with pseudo-dimension bounds that imply sample complexity guarantees.