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YouTubeDecision Discovery and Optimization Lab
Yen-An Lu: BO4IO: Bayesian Optimization for Inverse Optimization with Uncertainty Quantification
Abstract: Data-driven inverse optimization (IO) aims to estimate unknown parameters in an optimization model from observed decisions. The IO problem is commonly formulated as a large-scale bilevel program that is notoriously difficult to solve. We propose a derivative-free optimization approach based on Bayesian optimization, BO4IO, to solve ...
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