3 research outputs found
Information Directed Sampling for Linear Partial Monitoring
Partial monitoring is a rich framework for sequential decision making under
uncertainty that generalizes many well known bandit models, including linear,
combinatorial and dueling bandits. We introduce information directed sampling
(IDS) for stochastic partial monitoring with a linear reward and observation
structure. IDS achieves adaptive worst-case regret rates that depend on precise
observability conditions of the game. Moreover, we prove lower bounds that
classify the minimax regret of all finite games into four possible regimes. IDS
achieves the optimal rate in all cases up to logarithmic factors, without
tuning any hyper-parameters. We further extend our results to the contextual
and the kernelized setting, which significantly increases the range of possible
applications