NeurIPS 2018poster24 citations
Learning from discriminative feature feedback
Sanjoy Dasgupta, Akansha Dey, Nicholas Roberts, Sivan Sabato
Abstract
We consider the problem of learning a multi-class classifier from labels as well as simple explanations that we call "discriminative features". We show that such explanations can be provided whenever the target concept is a decision tree, or more generally belongs to a particular subclass of DNF formulas. We present an efficient online algorithm for learning from such feedback and we give tight bounds on the number of mistakes made during the learning process. These bounds depend only on the size of the target concept and not on the overall number of available features, which could be infinite. We also demonstrate the learning procedure experimentally.
BibTeX
@inproceedings{NEURIPS2018_36ac8e55,
author = {Dasgupta, Sanjoy and Dey, Akansha and Roberts, Nicholas and Sabato, Sivan},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Learning from discriminative feature feedback},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/36ac8e558ac7690b6f44e2cb5ef93322-Paper.pdf},
volume = {31},
year = {2018}
}