Fast and Accurate Ranking Regression

Ilkay Yildiz, Jennifer Dy, Deniz Erdogmus, Jayashree Kalpathy-Cramer, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis Ioannidis
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:77-88, 2020.

Abstract

We consider a ranking regression problem in which we use a dataset of ranked choices to learn Plackett-Luce scores as functions of sample features. We solve the maximum likelihood estimation problem by using the Alternating Directions Method of Multipliers (ADMM), effectively separating the learning of scores and model parameters. This separation allows us to express scores as the stationary distribution of a continuous-time Markov Chain. Using this equivalence, we propose two spectral algorithms for ranking regression that learn model parameters up to 579 times faster than the Newton’s method.

Cite this Paper


BibTeX
@InProceedings{pmlr-v108-yildiz20a, title = {Fast and Accurate Ranking Regression}, author = {Yildiz, Ilkay and Dy, Jennifer and Erdogmus, Deniz and Kalpathy-Cramer, Jayashree and Ostmo, Susan and Campbell, J. Peter and Chiang, Michael F. and Ioannidis, Stratis}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {77--88}, year = {2020}, editor = {Silvia Chiappa and Roberto Calandra}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/yildiz20a/yildiz20a.pdf}, url = { http://proceedings.mlr.press/v108/yildiz20a.html }, abstract = {We consider a ranking regression problem in which we use a dataset of ranked choices to learn Plackett-Luce scores as functions of sample features. We solve the maximum likelihood estimation problem by using the Alternating Directions Method of Multipliers (ADMM), effectively separating the learning of scores and model parameters. This separation allows us to express scores as the stationary distribution of a continuous-time Markov Chain. Using this equivalence, we propose two spectral algorithms for ranking regression that learn model parameters up to 579 times faster than the Newton’s method.} }
Endnote
%0 Conference Paper %T Fast and Accurate Ranking Regression %A Ilkay Yildiz %A Jennifer Dy %A Deniz Erdogmus %A Jayashree Kalpathy-Cramer %A Susan Ostmo %A J. Peter Campbell %A Michael F. Chiang %A Stratis Ioannidis %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-yildiz20a %I PMLR %P 77--88 %U http://proceedings.mlr.press/v108/yildiz20a.html %V 108 %X We consider a ranking regression problem in which we use a dataset of ranked choices to learn Plackett-Luce scores as functions of sample features. We solve the maximum likelihood estimation problem by using the Alternating Directions Method of Multipliers (ADMM), effectively separating the learning of scores and model parameters. This separation allows us to express scores as the stationary distribution of a continuous-time Markov Chain. Using this equivalence, we propose two spectral algorithms for ranking regression that learn model parameters up to 579 times faster than the Newton’s method.
APA
Yildiz, I., Dy, J., Erdogmus, D., Kalpathy-Cramer, J., Ostmo, S., Campbell, J.P., Chiang, M.F. & Ioannidis, S.. (2020). Fast and Accurate Ranking Regression. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:77-88 Available from http://proceedings.mlr.press/v108/yildiz20a.html .

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