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Tight Regret Bound for Online Inverse Linear Optimization via Multiscale Matrix Weights
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 over the same set. When the utility vector and the
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