UAI 2022poster5 citations
Multi-winner approval voting goes epistemic
Tahar Allouche, Jérôme Lang, Florian Yger
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).
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).}
}