# Skeptical inferences in multi-label ranking with sets of probabilities

arXiv: https://arxiv.org/abs/2210.08576 · HTML: https://mlsanity.com/paper/2210.08576/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 16 Oct 2022.

## Builds on (most cited first, 3 of 3)

- [Classifier Chains: A Review and Perspectives](https://mlsanity.com/paper/1912.13405/) (2019)
- [Online Boosting Algorithms for Multi-label Ranking](https://mlsanity.com/paper/1710.08079/) (2017)
- [Skeptical binary inferences in multi-label problems with sets of probabilities](https://mlsanity.com/paper/2205.00662/) (2022)

## Abstract (arXiv)

In this paper, we consider the problem of making skeptical inferences for the multi-label ranking problem. We assume that our uncertainty is described by a convex set of probabilities (i.e. a credal set), defined over the set of labels. Instead of learning a singleton prediction (or, a completed ranking over the labels), we thus seek for skeptical inferences in terms of set-valued predictions consisting of completed rankings.