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Tight Regret Bound for Online Inverse Linear Optimization via Multiscale Matrix Weights

arXiv:2609.26978v1 Announce Type: new Abstract: We study online inverse linear optimization with a fixed unknown linear utility: in each round, an environment presents a compact action set, the learner recommends an action from it, and the environment returns an action that maximizes the utility ov…

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  1. 2026-09-24 04:00 · arXiv stat.ML
    Tight Regret Bound for Online Inverse Linear Optimization via Multiscale Matrix Weights

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