Multi-winner approval voting goes epistemic

Tahar Allouche, Jérôme Lang, Florian Yger
Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, PMLR 180:75-84, 2022.

Abstract

Epistemic voting interprets votes as noisy signals about a ground truth. We consider contexts where the truth consists of a set of objective winners, knowing a lower and upper bound on its cardinality. A prototypical problem for this setting is the aggregation of multi-label annotations with prior knowledge on the size of the ground truth. We posit noise models, for which we define rules that output an optimal set of winners. We report on experiments on multi-label annotations (which we collected).

Cite this Paper

BibTeX
@InProceedings{pmlr-v180-allouche22a, title = {Multi-winner approval voting goes epistemic}, author = {Allouche, Tahar and Lang, J{\'e}r{\^o}me and Yger, Florian}, booktitle = {Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence}, pages = {75--84}, year = {2022}, editor = {Cussens, James and Zhang, Kun}, volume = {180}, series = {Proceedings of Machine Learning Research}, month = {01--05 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v180/allouche22a/allouche22a.pdf}, url = {https://proceedings.mlr.press/v180/allouche22a.html}, abstract = {Epistemic voting interprets votes as noisy signals about a ground truth. We consider contexts where the truth consists of a set of objective winners, knowing a lower and upper bound on its cardinality. A prototypical problem for this setting is the aggregation of multi-label annotations with prior knowledge on the size of the ground truth. We posit noise models, for which we define rules that output an optimal set of winners. We report on experiments on multi-label annotations (which we collected).} }
Endnote
%0 Conference Paper %T Multi-winner approval voting goes epistemic %A Tahar Allouche %A Jérôme Lang %A Florian Yger %B Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2022 %E James Cussens %E Kun Zhang %F pmlr-v180-allouche22a %I PMLR %P 75--84 %U https://proceedings.mlr.press/v180/allouche22a.html %V 180 %X Epistemic voting interprets votes as noisy signals about a ground truth. We consider contexts where the truth consists of a set of objective winners, knowing a lower and upper bound on its cardinality. A prototypical problem for this setting is the aggregation of multi-label annotations with prior knowledge on the size of the ground truth. We posit noise models, for which we define rules that output an optimal set of winners. We report on experiments on multi-label annotations (which we collected).
APA
Allouche, T., Lang, J. & Yger, F.. (2022). Multi-winner approval voting goes epistemic. Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 180:75-84 Available from https://proceedings.mlr.press/v180/allouche22a.html.

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