Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems

ArXi:2407.17200v3 Announce Type: replace-cross Many real-world decision problems require solving, again and again, combinatorial optimization instances drawn from a common distribution. A recent line of structured learning methods exploits this regularity by learning policies that pair a statistical model with a tractable combinatorial oracle, instead of solving each instance independently