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New bounds established for noiseless inverse optimization

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new theoretical bounds for noiseless inverse optimization (IO), a method used to infer a decision-maker's objective function from observed data. The study provides high-probability generalization bounds that are proportional to the number of unknown parameters and inversely proportional to the dataset size. These bounds are shown to be tight, matching existing results in bandit literature under certain conditions, and extend to both instantaneous and cumulative regret. AI

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RANK_REASON Academic paper published on arXiv detailing theoretical bounds for inverse optimization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New bounds established for noiseless inverse optimization

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Academic paper published on arXiv detailing theoretical bounds for inverse optimization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Pouria Fatemi, Hoomaan Maskan, Suvrit Sra, Peyman Mohajerin Esfahani ·

    Tight Generalization Bounds for Noiseless Inverse Optimization

    arXiv:2605.08866v2 Announce Type: replace Abstract: Inverse optimization (IO) seeks to infer the parameters of a decision-maker's objective from observed context--action data. We study noiseless IO, where demonstrations are generated by a ground-truth objective. We provide a high…

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