Conformal Poset Prediction for Label Ranking

Yusuf Sale, Eyke Hüllermeier
Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 329:1093-1095, 2026.

Abstract

In supervised label ranking, an input X $\in$ X is associated with a ranking $\Pi$ $\in$ Sm of a fixed label set Y = {1,...,m} (Fürnkranz and Hüllermeier, 2010). Write a $\succ$$\pi$ b when $\pi$ ranks a before b, and identify $\pi$ with {(a,b): a $\succ$$\pi$ b}. A ranker typically returns one permutation $\hat{\sigma}(X)$. Such a point prediction suppresses uncertainty: a model may be confident that a precedes c while having little evidence for the comparison between a and b, adjacent in $\hat{\sigma}(X)$. A natural output is therefore a strict partial order, which asserts only selected pairwise preferences and leaves the remaining pairs incomparable1. Our goal is to predict an informative partial order R(x) whose asserted comparisons are simultaneously correct, that is, P{R(Xn+1) $\subseteq$ $\Pi$n+1} $\geq$ 1 - $\alpha$.

Cite this Paper

BibTeX
@InProceedings{pmlr-v329-sale26a, title = {Conformal Poset Prediction for Label Ranking}, author = {Sale, Yusuf and H{\"u}llermeier, Eyke}, booktitle = {Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {1093--1095}, year = {2026}, editor = {Ahlberg, Ernst and Johansson, Ulf and Boström, Henrik and Carlevaro, Alberto and Hallberg Szabadváry, Johan and Carlsson, Lars}, volume = {329}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v329/main/assets/sale26a/sale26a.pdf}, url = {https://proceedings.mlr.press/v329/sale26a.html}, abstract = {In supervised label ranking, an input X $\in$ X is associated with a ranking $\Pi$ $\in$ Sm of a fixed label set Y = {1,...,m} (Fürnkranz and Hüllermeier, 2010). Write a $\succ$$\pi$ b when $\pi$ ranks a before b, and identify $\pi$ with {(a,b): a $\succ$$\pi$ b}. A ranker typically returns one permutation $\hat{\sigma}(X)$. Such a point prediction suppresses uncertainty: a model may be confident that a precedes c while having little evidence for the comparison between a and b, adjacent in $\hat{\sigma}(X)$. A natural output is therefore a strict partial order, which asserts only selected pairwise preferences and leaves the remaining pairs incomparable1. Our goal is to predict an informative partial order R(x) whose asserted comparisons are simultaneously correct, that is, P{R(Xn+1) $\subseteq$ $\Pi$n+1} $\geq$ 1 - $\alpha$.} }
Endnote
%0 Conference Paper %T Conformal Poset Prediction for Label Ranking %A Yusuf Sale %A Eyke Hüllermeier %B Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2026 %E Ernst Ahlberg %E Ulf Johansson %E Henrik Boström %E Alberto Carlevaro %E Johan Hallberg Szabadváry %E Lars Carlsson %F pmlr-v329-sale26a %I PMLR %P 1093--1095 %U https://proceedings.mlr.press/v329/sale26a.html %V 329 %X In supervised label ranking, an input X $\in$ X is associated with a ranking $\Pi$ $\in$ Sm of a fixed label set Y = {1,...,m} (Fürnkranz and Hüllermeier, 2010). Write a $\succ$$\pi$ b when $\pi$ ranks a before b, and identify $\pi$ with {(a,b): a $\succ$$\pi$ b}. A ranker typically returns one permutation $\hat{\sigma}(X)$. Such a point prediction suppresses uncertainty: a model may be confident that a precedes c while having little evidence for the comparison between a and b, adjacent in $\hat{\sigma}(X)$. A natural output is therefore a strict partial order, which asserts only selected pairwise preferences and leaves the remaining pairs incomparable1. Our goal is to predict an informative partial order R(x) whose asserted comparisons are simultaneously correct, that is, P{R(Xn+1) $\subseteq$ $\Pi$n+1} $\geq$ 1 - $\alpha$.
APA
Sale, Y. & Hüllermeier, E.. (2026). Conformal Poset Prediction for Label Ranking. Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 329:1093-1095 Available from https://proceedings.mlr.press/v329/sale26a.html.

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