Towards Black-box Iterative Machine Teaching
arXiv:1710.07742
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.
Published in ICML 2018
References in corpus (7)
- Distilling the Knowledge in a Neural Network
- An Overview of Machine Teaching
- Iterative Machine Teaching
- An Active Learning Algorithm for Ranking from Pairwise Preferences with an Almost Optimal Query Complexity
- Near-Optimally Teaching the Crowd to Classify
- The Teaching Dimension of Linear Learners
- Analysis of a Design Pattern for Teaching with Features and Labels