Online probabilistic label trees

Kalina Jasinska-Kobus, Marek Wydmuch, Devanathan Thiruvenkatachari, Krzysztof Dembczynski
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1801-1809, 2021.

Abstract

We introduce online probabilistic label trees (OPLTs), an algorithm that trains a label tree classifier in a fully online manner without any prior knowledge about the number of training instances, their features and labels. OPLTs are characterized by low time and space complexity as well as strong theoretical guarantees. They can be used for online multi-label and multi-class classification, including the very challenging scenarios of one- or few-shot learning. We demonstrate the attractiveness of OPLTs in a wide empirical study on several instances of the tasks mentioned above.

Cite this Paper

BibTeX
@InProceedings{pmlr-v130-jasinska-kobus21a, title = { Online probabilistic label trees }, author = {Jasinska-Kobus, Kalina and Wydmuch, Marek and Thiruvenkatachari, Devanathan and Dembczynski, Krzysztof}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, pages = {1801--1809}, year = {2021}, editor = {Banerjee, Arindam and Fukumizu, Kenji}, volume = {130}, series = {Proceedings of Machine Learning Research}, month = {13--15 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v130/jasinska-kobus21a/jasinska-kobus21a.pdf}, url = {https://proceedings.mlr.press/v130/jasinska-kobus21a.html}, abstract = { We introduce online probabilistic label trees (OPLTs), an algorithm that trains a label tree classifier in a fully online manner without any prior knowledge about the number of training instances, their features and labels. OPLTs are characterized by low time and space complexity as well as strong theoretical guarantees. They can be used for online multi-label and multi-class classification, including the very challenging scenarios of one- or few-shot learning. We demonstrate the attractiveness of OPLTs in a wide empirical study on several instances of the tasks mentioned above. } }
Endnote
%0 Conference Paper %T Online probabilistic label trees %A Kalina Jasinska-Kobus %A Marek Wydmuch %A Devanathan Thiruvenkatachari %A Krzysztof Dembczynski %B Proceedings of The 24th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2021 %E Arindam Banerjee %E Kenji Fukumizu %F pmlr-v130-jasinska-kobus21a %I PMLR %P 1801--1809 %U https://proceedings.mlr.press/v130/jasinska-kobus21a.html %V 130 %X We introduce online probabilistic label trees (OPLTs), an algorithm that trains a label tree classifier in a fully online manner without any prior knowledge about the number of training instances, their features and labels. OPLTs are characterized by low time and space complexity as well as strong theoretical guarantees. They can be used for online multi-label and multi-class classification, including the very challenging scenarios of one- or few-shot learning. We demonstrate the attractiveness of OPLTs in a wide empirical study on several instances of the tasks mentioned above.
APA
Jasinska-Kobus, K., Wydmuch, M., Thiruvenkatachari, D. & Dembczynski, K.. (2021). Online probabilistic label trees . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:1801-1809 Available from https://proceedings.mlr.press/v130/jasinska-kobus21a.html.

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