Teaching with Limited Information on the Learner's Behaviour

Jul 12, 2020

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Machine Teaching studies how efficiently a Teacher can guide a Learner to a target hypothesis. We focus on the model of Machine Teaching with a black box learner introduced in [Dasgupta et al., ICML 2019], where the teaching is done interactively without having any knowledge of the Learner's algorithm and class of hypotheses, apart from the fact that it contains the target hypothesis h^*. We first refine some existing results for this model and, then, we study new variants of it. Motivated by the realistic possibility that h^* is not available to the learner, we consider the case where the teacher can only aim at having the learner converge to a best available approximation of h^*. We also consider weaker black box learners, where, in each round, the choice of the consistent hypothesis returned to the Teacher is not adversarial, and in particular, we show that better provable bounds can be obtained for a type of Learner that moves to the next hypothesis smoothly, preferring hypotheses that are close to the current one; and for another type of Learner that can provide to the Teacher hypotheses chosen at random among those consistent with the examples received so far. Finally, we present an empirical evaluation of our basic interactive teacher on real datasets.

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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