Mallows ranking models: maximum likelihood estimate and regeneration

Wenpin Tang
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6125-6134, 2019.

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.

Cite this Paper

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.} }
Endnote
%0 Conference Paper %T Mallows ranking models: maximum likelihood estimate and regeneration %A Wenpin Tang %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-tang19a %I PMLR %P 6125--6134 %U https://proceedings.mlr.press/v97/tang19a.html %V 97 %X 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.
APA
Tang, W.. (2019). Mallows ranking models: maximum likelihood estimate and regeneration. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:6125-6134 Available from https://proceedings.mlr.press/v97/tang19a.html.

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