Preprint
Aug 2026
An inverse mixed-integer optimization framework for learning interpretable models of expert decision making
This work develops an inverse optimization approach to jointly learn the decision-maker's preferences and the decision rules governing their choices, which leads to better predictions and greater flexibility in capturing and replicating expert decision making.
Anurag Holani, Rishabh Gupta, J. Wassick et al.
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