Teacher improves learning by selecting a training subset
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...
Main Authors: | Ma, Y, Nowak, R, Rigollet, P, Zhang, X, Zhu, X |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Mathematics |
Format: | Article |
Language: | English |
Published: |
2021
|
Online Access: | https://hdl.handle.net/1721.1/137038 |
Similar Items
-
Subset selection in regression /
by: 343814 Miller, Alan J.
Published: (1990) -
An improved parallelized MRMR for gene subset selection for cancer classification /
by: Rohani Mohammad Kusairi, 1993- author, et al.
Published: (2016) -
An improved parallelized MRMR for gene subset selection for cancer classification /
by: Rohani Mohammad Kusairi, 1993- author
Published: (2016) -
An improved parallelized mRMR for gene subset selection in cancer classification
by: Kusairi, R. M., et al.
Published: (2017) -
An improved parallelized mRMR for gene subset selection in cancer classification
by: Kusairi, R.M., et al.
Published: (2017)