AI RESEARCH
Tight Generalization Bounds for Noiseless Inverse Optimization
arXiv CS.LG
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ArXi:2605.08866v1 Announce Type: cross Inverse optimization (IO) seeks to infer the parameters of a decision-maker's objective from observed context--action data. We study noiseless IO, where nstrations are generated by a ground-truth objective. We provide a high-probability ${O}(\frac{d}{T})$ generalization bound for the induced action set, where $d$ is the number of unknown parameters and $T$ is the size of the