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- W2952320247 abstract "We revisit the study of optimal regret rates in bandit combinatorial optimization---a fundamental framework for sequential decision making under uncertainty that abstracts numerous combinatorial prediction problems. We prove that the attainable regret in this setting grows as $widetilde{Theta}(k^{3/2}sqrt{dT})$ where $d$ is the dimension of the problem and $k$ is a bound over the maximal instantaneous loss, disproving a conjecture of Audibert, Bubeck, and Lugosi (2013) who argued that the optimal rate should be of the form $widetilde{Theta}(ksqrt{dT})$. Our bounds apply to several important instances of the framework, and in particular, imply a tight bound for the well-studied bandit shortest path problem. By that, we also resolve an open problem posed by Cesa-Bianchi and Lugosi (2012)." @default.
- W2952320247 created "2019-06-27" @default.
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- W2952320247 date "2017-02-24" @default.
- W2952320247 modified "2023-09-27" @default.
- W2952320247 title "Tight Bounds for Bandit Combinatorial Optimization" @default.
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