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Bo Lin

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Conformal Inverse Optimization for Adherence-Aware Prescriptive Analytics
Timothy C. Y. Chan
Erick Delage
Bo Lin
When “Optimal” Advice Gets Ignored Algorithms can recommend decisions that look optimal on paper but conflict with how people assess the… (voir plus) situation, making those recommendations less likely to be followed. In “Conformal Inverse Optimization for Adherence-Aware Prescriptive Analytics,” Chan, Delage, and Lin address this challenge by using past decisions to learn how people evaluate different options. Because those decisions may reflect diverse preferences, noisy observations, or imperfect models, the authors do not rely on a single estimated preference profile. Instead, they construct an uncertainty set around the inferred parameters and use robust optimization to generate recommendations that perform well both objectively and from the decision maker’s perspective. Their method comes with statistical and performance guarantees and outperforms standard inverse optimization in numerical experiments. In a simulated Toronto food-delivery study, it substantially improves estimated courier adherence, maintaining comparable delivery times, showing how better-aligned recommendations can benefit both users and organizations.
2026-09-15
Operations Research (publié)
doi.org
Conformal Inverse Optimization
Bo Lin
Erick Delage
Timothy Chan
Inverse optimization has been increasingly used to estimate unknown parameters in an optimization model based on decision data. We show that… (voir plus) such a point estimation is insufficient in a prescriptive setting where the estimated parameters are used to prescribe new decisions. The prescribed decisions may be low-quality and misaligned with human intuition and thus are unlikely to be adopted. To tackle this challenge, we propose conformal inverse optimization, which seeks to learn an uncertainty set for the unknown parameters and then solve a robust optimization model to prescribe new decisions. Under mild assumptions, we show that our method enjoys provable guarantees on solution quality, as evaluated using both the ground-truth parameters and the decision maker's perception of the unknown parameters. Our method demonstrates strong empirical performance compared to classic inverse optimization.
2024-09-24
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net