Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
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).
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Baihan Lin, Guillermo Cecchi, et al.
IJCAI 2023
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks
Barry K. Rosen
SWAT 1972