Learning with Feature Feedback: from Theory to Practice

Stefanos Poulis, Sanjoy Dasgupta
; Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:1104-1113, 2017.

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 this Paper


BibTeX
@InProceedings{pmlr-v54-poulis17a, title = {{Learning with Feature Feedback: from Theory to Practice}}, author = {Stefanos Poulis and Sanjoy Dasgupta}, booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics}, pages = {1104--1113}, year = {2017}, editor = {Aarti Singh and Jerry Zhu}, volume = {54}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {20--22 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v54/poulis17a/poulis17a.pdf}, url = {http://proceedings.mlr.press/v54/poulis17a.html}, 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.} }
Endnote
%0 Conference Paper %T Learning with Feature Feedback: from Theory to Practice %A Stefanos Poulis %A Sanjoy Dasgupta %B Proceedings of the 20th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2017 %E Aarti Singh %E Jerry Zhu %F pmlr-v54-poulis17a %I PMLR %J Proceedings of Machine Learning Research %P 1104--1113 %U http://proceedings.mlr.press %V 54 %W PMLR %X 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.
APA
Poulis, S. & Dasgupta, S.. (2017). Learning with Feature Feedback: from Theory to Practice. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, in PMLR 54:1104-1113

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