A multi-armed bandit (MAB) problem is one of the classic models of sequential decision making (Auer et al., 2002; Agrawal and Goyal, 2012, 2013a).

Our primary focus is the identification of the latent structure in measurement models without parametric assumptions such as linearity or Gaussianity.

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Although this verification problem is NP-complete in general, we give a more precise complexity characterization. We show that there is a simple linear time algorithm for verifying a single tree, and for tree ensembles the verification problem can be cast as a max-clique problem on a multi-partite graph withbounded boxicity. Forlowdimensional problems when boxicity can be viewed as constant, this reformulation leads to a polynomial time algorithm.

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Here, θ() is the clique coveringnumberofthegraph. Online learning models a repeated interaction between a learner and an environment.

Our empirical results demonstrate that our method outperforms representative GNN baselines in terms of solution accuracy and inference speed as well asconventional solverslikeGurobi with limited time budgets.