Teacher improves learning by selecting a training subset
Name
ma18a.pdf
Description
Published version
Size
638.84 KB
Format
Adobe PDF
Checksum (MD5)
fad2d74b70fc54d3b2cedfccba4c8a24
Author(s)
Ma, Y
Nowak, R
Rigollet, P
Zhang, X
Zhu, X
Date Issued
2018
Journal
International Conference on Artificial Intelligence and Statistics, AISTATS 2018
Citation
Ma, Y, Nowak, R, Rigollet, P, Zhang, X and Zhu, X. 2018. "Teacher improves learning by selecting a training subset." International Conference on Artificial Intelligence and Statistics, AISTATS 2018, 84.
Version
Final published version
Abstract
Copyright 2018 by the author(s). 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.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://proceedings.mlr.press/v84/ma18a.html
