ICML 2019oral29 citations
Mallows ranking models: maximum likelihood estimate and regeneration
Abstract
This paper is concerned with various Mallows ranking models. We study the statistical properties of the MLE of Mallows’ $\phi$ model. We also make connections of various Mallows ranking models, encompassing recent progress in mathematics. Motivated by the infinite top-$t$ ranking model, we propose an algorithm to select the model size $t$ automatically. The key idea relies on the renewal property of such an infinite random permutation. Our algorithm shows good performance on several data sets.
BibTeX
@InProceedings{pmlr-v97-tang19a,
title = {Mallows ranking models: maximum likelihood estimate and regeneration},
author = {Tang, Wenpin},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {6125--6134},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/tang19a/tang19a.pdf},
url = {https://proceedings.mlr.press/v97/tang19a.html},
abstract = {This paper is concerned with various Mallows ranking models. We study the statistical properties of the MLE of Mallows’ $\phi$ model. We also make connections of various Mallows ranking models, encompassing recent progress in mathematics. Motivated by the infinite top-$t$ ranking model, we propose an algorithm to select the model size $t$ automatically. The key idea relies on the renewal property of such an infinite random permutation. Our algorithm shows good performance on several data sets.}
}