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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 actions lie in the $...
A learning algorithm for general optimization problems with linear objective functions that eliminates the need to solve inverse optimization problems, and achieves competitive performance in terms of regret compared with existing methods, while offering improved computational efficiency for certain classes of downstre...
This analysis builds on the biased-metric viewpoint behind the recent improvement over the $3$ barrier and proves a stability property for the biased-metric ratio.
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