Teacher Improves Learning by Selecting a Training Subset
Abstract
We call a learner super-teachable if a teacher can trim down an iid training set while making the learner learn even better. We provide sharp super-teaching guarantees on two learners: the maximum likelihood estimator for the mean of a Gaussian, and the large margin classifier in 1D. For general learners, we provide a mixed-integer nonlinear programming-based algorithm to find a super teaching set. Empirical experiments show that our algorithm is able to find good super-teaching sets for both regression and classification problems.
Cite
Text
Ma et al. "Teacher Improves Learning by Selecting a Training Subset." International Conference on Artificial Intelligence and Statistics, 2018.Markdown
[Ma et al. "Teacher Improves Learning by Selecting a Training Subset." International Conference on Artificial Intelligence and Statistics, 2018.](https://mlanthology.org/aistats/2018/ma2018aistats-teacher/)BibTeX
@inproceedings{ma2018aistats-teacher,
title = {{Teacher Improves Learning by Selecting a Training Subset}},
author = {Ma, Yuzhe and Nowak, Robert and Rigollet, Philippe and Zhang, Xuezhou and Zhu, Xiaojin},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2018},
pages = {1366-1375},
url = {https://mlanthology.org/aistats/2018/ma2018aistats-teacher/}
}