Browsing by Subject "Adversarial multi-armed bandit"
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Item Open Access An efficient bandit algorithm for general weight assignments(IEEE, 2017) Gökçesu, Kaan; Ergen, Tolga; Çiftçi, S.; Kozat, Süleyman SerdarIn this paper, we study the adversarial multi armed bandit problem and present a generally implementable efficient bandit arm selection structure. Since we do not have any statistical assumptions on the bandit arm losses, the results in the paper are guaranteed to hold in an individual sequence manner. The introduced framework is able to achieve the optimal regret bounds by employing general weight assignments on bandit arm selection sequences. Hence, this framework can be used for a wide range of applications.Item Open Access Minimax optimal algorithms for adversarial bandit problem with multiple plays(IEEE, 2019) Vural, Nuri Mert; Gökçesu, Hakan; Gökçesu, K.; Kozat, Süleyman SerdarWe investigate the adversarial bandit problem with multiple plays under semi-bandit feedback. We introduce a highly efficient algorithm that asymptotically achieves the performance of the best switching m-arm strategy with minimax optimal regret bounds. To construct our algorithm, we introduce a new expert advice algorithm for the multiple-play setting. By using our expert advice algorithm, we additionally improve the best-known high-probability bound for the multi-play setting by O(√(m)). Our results are guaranteed to hold in an individual sequence manner since we have no statistical assumption on the bandit arm gains. Through an extensive set of experiments involving synthetic and real data, we demonstrate significant performance gains achieved by the proposed algorithm with respect to the state-of-the-art algorithms.