Distributionally Robust, Skeptical Binary Inferences in Multi-label Problems

Yonatan Carlos Carranza Alarcón, Sébastien Destercke
Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, PMLR 147:51-60, 2021.

Abstract

In this paper, we consider the problem of making distributionally robust, skeptical inferences for the multi-label problem, or more generally for Boolean vectors. By distributionally robust, we mean that we consider sets of probability distributions, and by skeptical we understand that we consider as valid only those inferences that are true for every distribution within this set. Such inferences will provide partial predictions whenever the considered set is sufficiently big. We study in particular the Hamming loss case, a common loss function in multi-label problems, showing how skeptical inferences can be made in this setting. We also perform some experiments demonstrating the interest of our results.

Cite this Paper

BibTeX
@InProceedings{pmlr-v147-carranza-alarcon21a, title = {Distributionally Robust, Skeptical Binary Inferences in Multi-label Problems}, author = {Carranza Alarc\'on, Yonatan Carlos and Destercke, S\'ebastien}, booktitle = {Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications}, pages = {51--60}, year = {2021}, editor = {Cano, Andrés and De Bock, Jasper and Miranda, Enrique and Moral, Serafı́n}, volume = {147}, series = {Proceedings of Machine Learning Research}, month = {06--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v147/carranza-alarcon21a/carranza-alarcon21a.pdf}, url = {https://proceedings.mlr.press/v147/carranza-alarcon21a.html}, abstract = {In this paper, we consider the problem of making distributionally robust, skeptical inferences for the multi-label problem, or more generally for Boolean vectors. By distributionally robust, we mean that we consider sets of probability distributions, and by skeptical we understand that we consider as valid only those inferences that are true for every distribution within this set. Such inferences will provide partial predictions whenever the considered set is sufficiently big. We study in particular the Hamming loss case, a common loss function in multi-label problems, showing how skeptical inferences can be made in this setting. We also perform some experiments demonstrating the interest of our results.} }
Endnote
%0 Conference Paper %T Distributionally Robust, Skeptical Binary Inferences in Multi-label Problems %A Yonatan Carlos Carranza Alarcón %A Sébastien Destercke %B Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications %C Proceedings of Machine Learning Research %D 2021 %E Andrés Cano %E Jasper De Bock %E Enrique Miranda %E Serafı́n Moral %F pmlr-v147-carranza-alarcon21a %I PMLR %P 51--60 %U https://proceedings.mlr.press/v147/carranza-alarcon21a.html %V 147 %X In this paper, we consider the problem of making distributionally robust, skeptical inferences for the multi-label problem, or more generally for Boolean vectors. By distributionally robust, we mean that we consider sets of probability distributions, and by skeptical we understand that we consider as valid only those inferences that are true for every distribution within this set. Such inferences will provide partial predictions whenever the considered set is sufficiently big. We study in particular the Hamming loss case, a common loss function in multi-label problems, showing how skeptical inferences can be made in this setting. We also perform some experiments demonstrating the interest of our results.
APA
Carranza Alarcón, Y.C. & Destercke, S.. (2021). Distributionally Robust, Skeptical Binary Inferences in Multi-label Problems. Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, in Proceedings of Machine Learning Research 147:51-60 Available from https://proceedings.mlr.press/v147/carranza-alarcon21a.html.

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