Learning with Feature Feedback: From Theory to Practice
Abstract
In supervised learning, a human annotator only needs to assign each data point (document, image, etc.) its correct label. But in many situations, the human can also provide richer feedback at essentially no extra cost. In this paper, we examine a particular type of feature feedback that has been used, with some success, in information retrieval and in computer vision. We formalize two models of feature feedback, give learning algorithms for them, and quantify their usefulness in the learning process. Our experiments also show the efficacy of these methods.
Cite
Text
Poulis and Dasgupta. "Learning with Feature Feedback: From Theory to Practice." International Conference on Artificial Intelligence and Statistics, 2017.Markdown
[Poulis and Dasgupta. "Learning with Feature Feedback: From Theory to Practice." International Conference on Artificial Intelligence and Statistics, 2017.](https://mlanthology.org/aistats/2017/poulis2017aistats-learning/)BibTeX
@inproceedings{poulis2017aistats-learning,
title = {{Learning with Feature Feedback: From Theory to Practice}},
author = {Poulis, Stefanos and Dasgupta, Sanjoy},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2017},
pages = {1104-1113},
url = {https://mlanthology.org/aistats/2017/poulis2017aistats-learning/}
}