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Machine teaching has traditionally been constrained by the assumption of a fixed learner’s model. In this paper, we challenge this notion by proposing a novel black-box Markov learner model, drawing inspiration from decision psychology and neuroscience where learners are often viewed as black boxes with adaptable parameters. We model the learner’s dynamics as a Markov decision process (MDP) with unknown parameters, encompassing a wide range of learner types studied in machine teaching literature. This approach reduces teaching complexity to finding an optimal policy for the underlying MDP. Building on this, we introduce an algorithm for teaching in this black-box setting and provide an analysis of teaching costs under different scenarios. We further establish a connection between our model and two types of learners in psychology and neuroscience, the epiphany learner and the non-epiphany learner, linking them with discounted and non-discounted black-box Markov learners respectively. This alignment offers a psychologically and neuroscientifically grounded perspective to our work. Supported by numerical study results, this paper delivers a significant contribution to machine teaching, introducing a robust, versatile learner model with a rigorous theoretical foundation. 

How can we collect the most useful labels to learn a model selection policy, when presented with arbitrary heterogeneous data streams? In this paper, we formulate this task as a contextual active model selection problem, where at each round the learner receives an unlabeled data point along with a context. The goal is to output the best model for any given context without obtaining an excessive amount of labels. In particular, we focus on the task of selecting pre-trained classifiers, and propose a contextual active model selection algorithm (CAMS), which relies on a novel uncertainty sampling query criterion defined on a given policy class for adaptive model selection. In comparison to prior art, our algorithm does not assume a globally optimal model. We provide rigorous theoretical analysis for the regret and query complexity under both adversarial and stochastic settings. Our experiments on several benchmark classification datasets demonstrate the algorithm’s effectiveness in terms of both regret and query complexity. Notably, to achieve the same accuracy, CAMS incurs less than 10% of the label cost when compared to the best online model selection baselines on CIFAR10.