Guo-Jun Qi, Charu Aggarwal, et al.
IEEE TPAMI
We study on-line decision problems where the set of actions that are available to the decision algorithm varies over time. With a few notable exceptions, such problems remained largely unaddressed in the literature, despite their applicability to a large number of practical problems. Departing from previous work on this "Sleeping Experts" problem, we compare algorithms against the payoff obtained by the best ordering of the actions, which is a natural benchmark for this type of problem. We study both the full-information (best expert) and partial-information (multi-armed bandit) settings and consider both stochastic and adversarial rewards models. For all settings we give algorithms achieving (almost) information-theoretically optimal regret bounds (up to a constant or a sub-logarithmic factor) with respect to the best-ordering benchmark. © 2010 The Author(s).
Guo-Jun Qi, Charu Aggarwal, et al.
IEEE TPAMI
Shai Fine, Yishay Mansour
Machine Learning
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CLEO 2023
Victor Akinwande, Megan Macgregor, et al.
IJCAI 2024