Iterative Teaching by Label SynthesisDownload PDF

21 May 2021, 20:42 (modified: 09 Nov 2021, 18:58)NeurIPS 2021 SpotlightReaders: Everyone
Keywords: Iterative Machine Teaching, Reinforcement Learning, Unrolling, Label
TL;DR: An iterative teaching framework via label synthesis.
Abstract: In this paper, we consider the problem of iterative machine teaching, where a teacher provides examples sequentially based on the current iterative learner. In contrast to previous methods that have to scan over the entire pool and select teaching examples from it in each iteration, we propose a label synthesis teaching framework where the teacher randomly selects input teaching examples (e.g., images) and then synthesizes suitable outputs (e.g., labels) for them. We show that this framework can avoid costly example selection while still provably achieving exponential teachability. We propose multiple novel teaching algorithms in this framework. Finally, we empirically demonstrate the value of our framework.
Supplementary Material: pdf
Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
19 Replies

Loading