Active Learning with Disagreement Graphs

Corinna Cortes, Giulia Desalvo, Mehryar Mohri, Ningshan Zhang, Claudio Gentile
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1379-1387, 2019.

Abstract

We present two novel enhancements of an online importance-weighted active learning algorithm IWAL, using the properties of disagreements among hypotheses. The first enhancement, IWALD, prunes the hypothesis set with a more aggressive strategy based on the disagreement graph. We show that IWAL-D improves the generalization performance and the label complexity of the original IWAL, and quantify the improvement in terms of the disagreement graph coefficient. The second enhancement, IZOOM, further improves IWAL-D by adaptively zooming into the current version space and thus reducing the best-in-class error. We show that IZOOM admits favorable theoretical guarantees with the changing hypothesis set. We report experimental results on multiple datasets and demonstrate that the proposed algorithms achieve better test performances than IWAL given the same amount of labeling budget.

Cite this Paper

BibTeX
@InProceedings{pmlr-v97-cortes19b, title = {Active Learning with Disagreement Graphs}, author = {Cortes, Corinna and Desalvo, Giulia and Mohri, Mehryar and Zhang, Ningshan and Gentile, Claudio}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, pages = {1379--1387}, year = {2019}, editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan}, volume = {97}, series = {Proceedings of Machine Learning Research}, month = {09--15 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v97/cortes19b/cortes19b.pdf}, url = {https://proceedings.mlr.press/v97/cortes19b.html}, abstract = {We present two novel enhancements of an online importance-weighted active learning algorithm IWAL, using the properties of disagreements among hypotheses. The first enhancement, IWALD, prunes the hypothesis set with a more aggressive strategy based on the disagreement graph. We show that IWAL-D improves the generalization performance and the label complexity of the original IWAL, and quantify the improvement in terms of the disagreement graph coefficient. The second enhancement, IZOOM, further improves IWAL-D by adaptively zooming into the current version space and thus reducing the best-in-class error. We show that IZOOM admits favorable theoretical guarantees with the changing hypothesis set. We report experimental results on multiple datasets and demonstrate that the proposed algorithms achieve better test performances than IWAL given the same amount of labeling budget.} }
Endnote
%0 Conference Paper %T Active Learning with Disagreement Graphs %A Corinna Cortes %A Giulia Desalvo %A Mehryar Mohri %A Ningshan Zhang %A Claudio Gentile %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-cortes19b %I PMLR %P 1379--1387 %U https://proceedings.mlr.press/v97/cortes19b.html %V 97 %X We present two novel enhancements of an online importance-weighted active learning algorithm IWAL, using the properties of disagreements among hypotheses. The first enhancement, IWALD, prunes the hypothesis set with a more aggressive strategy based on the disagreement graph. We show that IWAL-D improves the generalization performance and the label complexity of the original IWAL, and quantify the improvement in terms of the disagreement graph coefficient. The second enhancement, IZOOM, further improves IWAL-D by adaptively zooming into the current version space and thus reducing the best-in-class error. We show that IZOOM admits favorable theoretical guarantees with the changing hypothesis set. We report experimental results on multiple datasets and demonstrate that the proposed algorithms achieve better test performances than IWAL given the same amount of labeling budget.
APA
Cortes, C., Desalvo, G., Mohri, M., Zhang, N. & Gentile, C.. (2019). Active Learning with Disagreement Graphs. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:1379-1387 Available from https://proceedings.mlr.press/v97/cortes19b.html.

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