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The results show that sharing observations across objectives and ignoring irrelevant objective-wise distinctions can substantially reduce the sampling cost, as confirmed by experiments on synthetic and real-world tasks.
An inverse optimization perspective for OM is provided by reformulating it as an opinion minimization (OMin) problem, whose objective function holds succinct expression and desired properties and introduces a bounded estimation scheme (BES) that can estimate the objective function of OMin with bounded error in Õ(kn+m)...
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