Variational Inference from Ranked Samples with Features
Abstract
In many supervised learning settings, elicited labels comprise pairwise comparisons or rankings of samples. We propose a Bayesian inference model for ranking datasets, allowing us to take a probabilistic approach to ranking inference. Our probabilistic assumptions are motivated by, and consistent with, the so-called Plackett-Luce model. We propose a variational inference method to extract a closed-form Gaussian posterior distribution. We show experimentally that the resulting posterior yields more reliable ranking predictions compared to predictions via point estimates.
Cite
Text
Guo et al. "Variational Inference from Ranked Samples with Features." Proceedings of The Eleventh Asian Conference on Machine Learning, 2019.Markdown
[Guo et al. "Variational Inference from Ranked Samples with Features." Proceedings of The Eleventh Asian Conference on Machine Learning, 2019.](https://mlanthology.org/acml/2019/guo2019acml-variational/)BibTeX
@inproceedings{guo2019acml-variational,
title = {{Variational Inference from Ranked Samples with Features}},
author = {Guo, Yuan and Dy, Jennifer and Erdoğmuş, Deniz and Kalpathy-Cramer, Jayashree and Ostmo, Susan and Campbell, J. Peter and Chiang, Michael F. and Ioannidis, Stratis},
booktitle = {Proceedings of The Eleventh Asian Conference on Machine Learning},
year = {2019},
pages = {599-614},
volume = {101},
url = {https://mlanthology.org/acml/2019/guo2019acml-variational/}
}