Negative Results for Active Learning with Convex Losses

Steve Hanneke, Liu Yang
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:321-325, 2010.

Abstract

We study the problem of active learning with convex loss functions. We prove that even under bounded noise constraints, the minimax rates for proper active learning are often no better than passive learning.

Cite this Paper

BibTeX
@InProceedings{pmlr-v9-hanneke10a, title = {Negative Results for Active Learning with Convex Losses}, author = {Hanneke, Steve and Yang, Liu}, booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics}, pages = {321--325}, year = {2010}, editor = {Teh, Yee Whye and Titterington, Mike}, volume = {9}, series = {Proceedings of Machine Learning Research}, address = {Chia Laguna Resort, Sardinia, Italy}, month = {13--15 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v9/hanneke10a/hanneke10a.pdf}, url = {https://proceedings.mlr.press/v9/hanneke10a.html}, abstract = {We study the problem of active learning with convex loss functions. We prove that even under bounded noise constraints, the minimax rates for proper active learning are often no better than passive learning.} }
Endnote
%0 Conference Paper %T Negative Results for Active Learning with Convex Losses %A Steve Hanneke %A Liu Yang %B Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2010 %E Yee Whye Teh %E Mike Titterington %F pmlr-v9-hanneke10a %I PMLR %P 321--325 %U https://proceedings.mlr.press/v9/hanneke10a.html %V 9 %X We study the problem of active learning with convex loss functions. We prove that even under bounded noise constraints, the minimax rates for proper active learning are often no better than passive learning.
RIS
TY - CPAPER TI - Negative Results for Active Learning with Convex Losses AU - Steve Hanneke AU - Liu Yang BT - Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics DA - 2010/03/31 ED - Yee Whye Teh ED - Mike Titterington ID - pmlr-v9-hanneke10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 9 SP - 321 EP - 325 L1 - http://proceedings.mlr.press/v9/hanneke10a/hanneke10a.pdf UR - https://proceedings.mlr.press/v9/hanneke10a.html AB - We study the problem of active learning with convex loss functions. We prove that even under bounded noise constraints, the minimax rates for proper active learning are often no better than passive learning. ER -
APA
Hanneke, S. & Yang, L.. (2010). Negative Results for Active Learning with Convex Losses. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 9:321-325 Available from https://proceedings.mlr.press/v9/hanneke10a.html.

Related Material