Combinatorial Multi-Armed Bandit: General Framework and Applications

By Yajun Wang, Yang Yuan. International Conference on Machine Learning, 2013

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Efficient Contextual Semi-Bandit Learning (2015, background): “There is a growing body of work on combinatorial bandits, which are also referred to as semi-bandits, or slate bandits [György et al., 2007, Uchiya et al., 2010, Kale et al., 2010, Audibert et al., 2011, 2014, Kveton et al., 2014, Chen et al., 2013, 2014, Qin et al., 2014, Kveton et al., 2015].”
Introduction to Multi-Armed Bandits (2019, background): “ parameters such as dand the maximal number of atoms in an action. Such improvements are possible when actions correspond to “slates” of search results (Kale et al., 2010), and for IID rewards (e.g., Chen et al., 2013; Kveton et al., 2015c, 2014a). A similar but technically different cascade model p”
Contextual Combinatorial Bandit and its Application on Diversified Online Recommendation (2014, background): “Recently, several work generalized the classical non-contextual bandits to combinatorial bandits [10, 11, 7], where the learner can play a set of arms, which is termed as a super arm, on each round.”