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- W4287130135 abstract "We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form $f_t(x) = g_t(langle x, thetarangle)$ for convex $g_t : mathbb R to mathbb R$ and unknown $theta in mathbb R^d$ that is homogeneous over time. We provide a short information-theoretic proof that the minimax regret is at most $O(d sqrt{n} log(n operatorname{diam}(mathcal K)))$ where $n$ is the number of interactions, $d$ the dimension and $operatorname{diam}(mathcal K)$ is the diameter of the constraint set." @default.
- W4287130135 created "2022-07-25" @default.
- W4287130135 creator A5017031238 @default.
- W4287130135 date "2021-06-01" @default.
- W4287130135 modified "2023-09-27" @default.
- W4287130135 title "Minimax Regret for Bandit Convex Optimisation of Ridge Functions" @default.
- W4287130135 doi "https://doi.org/10.48550/arxiv.2106.00444" @default.
- W4287130135 hasPublicationYear "2021" @default.
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