A zero-loss equivalence class of cost estimators makes decision-focused learning fail in shortest-path network interdiction games, and training on interdicted scenarios (A-DFL) collapses this class and restores performance.
IEEE Transactions on Control of Network Systems7(4), 1585–1596 (2020)
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Decision-Focused Learning in Network Interdiction Games
A zero-loss equivalence class of cost estimators makes decision-focused learning fail in shortest-path network interdiction games, and training on interdicted scenarios (A-DFL) collapses this class and restores performance.