Extends PAC machine teaching to handle deductive errors by requiring teachers to select sets that lead to approximately correct hypotheses with high probability despite learner mistakes, with complexity results and LLM experiments.
Towards Black-box Iterative Machine Teaching
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abstract
In this paper, we make an important step towards the black-box machine teaching by considering the cross-space machine teaching, where the teacher and the learner use different feature representations and the teacher can not fully observe the learner's model. In such scenario, we study how the teacher is still able to teach the learner to achieve faster convergence rate than the traditional passive learning. We propose an active teacher model that can actively query the learner (i.e., make the learner take exams) for estimating the learner's status and provably guide the learner to achieve faster convergence. The sample complexities for both teaching and query are provided. In the experiments, we compare the proposed active teacher with the omniscient teacher and verify the effectiveness of the active teacher model.
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cs.LG 1years
2026 1verdicts
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Teaching and Learning under Deductive Errors
Extends PAC machine teaching to handle deductive errors by requiring teachers to select sets that lead to approximately correct hypotheses with high probability despite learner mistakes, with complexity results and LLM experiments.