Web: http://arxiv.org/abs/2209.06983

Sept. 16, 2022, 1:13 a.m. | Wonyoung Kim, Kyungbok Lee, Myunghee Cho Paik

stat.ML updates on arXiv.org arxiv.org

We propose a novel contextual bandit algorithm for generalized linear rewards
with an $\tilde{O}(\sqrt{\kappa^{-1} \phi T})$ regret over $T$ rounds where
$\phi$ is the minimum eigenvalue of the covariance of contexts and $\kappa$ is
a lower bound of the variance of rewards. In several practical cases where
$\phi=O(d)$, our result is the first regret bound for generalized linear model
(GLM) bandits with the order $\sqrt{d}$ without relying on the approach of Auer
[2002]. We achieve this bound using a novel …

arxiv linear sampling

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