Position-Aware ListMLE: A Sequential Learning Process for Ranking

Yanyan Lan ICT, Yadong Zhu ICT, Jiafeng Guo ICT, Shuzi Niu ICT, Xueqi Cheng ICT
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:529-538, 2014.

Abstract

Communication costs, resulting from synchro- nization requirements during learning, can greatly slow down many parallel machine learning algorithms. In this paper, we present a parallel Markov chain Monte Carlo (MCMC) algorithm in which subsets of data are pro- cessed independently, with very little com- munication. First, we arbitrarily partition data onto multiple machines. Then, on each machine, any classical MCMC method (e.g., Gibbs sampling) may be used to draw samples from a posterior distribution given the data subset. Finally, the samples from each ma- chine are combined to form samples from the full posterior. This embarrassingly parallel algorithm allows each machine to act inde- pendently on a subset of the data (without communication) until the final combination stage. We prove that our algorithm generates asymptotically exact samples and empirically demonstrate its ability to parallelize burn-in and sampling in several models.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-ict14a, title = {Position-Aware ListMLE: A Sequential Learning Process for Ranking}, author = {ICT, Yanyan Lan and ICT, Yadong Zhu and ICT, Jiafeng Guo and ICT, Shuzi Niu and ICT, Xueqi Cheng}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {529--538}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/ict14a/ict14a.pdf}, url = {https://proceedings.mlr.press/r12/ict14a.html}, abstract = {Communication costs, resulting from synchro- nization requirements during learning, can greatly slow down many parallel machine learning algorithms. In this paper, we present a parallel Markov chain Monte Carlo (MCMC) algorithm in which subsets of data are pro- cessed independently, with very little com- munication. First, we arbitrarily partition data onto multiple machines. Then, on each machine, any classical MCMC method (e.g., Gibbs sampling) may be used to draw samples from a posterior distribution given the data subset. Finally, the samples from each ma- chine are combined to form samples from the full posterior. This embarrassingly parallel algorithm allows each machine to act inde- pendently on a subset of the data (without communication) until the final combination stage. We prove that our algorithm generates asymptotically exact samples and empirically demonstrate its ability to parallelize burn-in and sampling in several models.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Position-Aware ListMLE: A Sequential Learning Process for Ranking %A Yanyan Lan ICT %A Yadong Zhu ICT %A Jiafeng Guo ICT %A Shuzi Niu ICT %A Xueqi Cheng ICT %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-ict14a %I PMLR %P 529--538 %U https://proceedings.mlr.press/r12/ict14a.html %V R12 %X Communication costs, resulting from synchro- nization requirements during learning, can greatly slow down many parallel machine learning algorithms. In this paper, we present a parallel Markov chain Monte Carlo (MCMC) algorithm in which subsets of data are pro- cessed independently, with very little com- munication. First, we arbitrarily partition data onto multiple machines. Then, on each machine, any classical MCMC method (e.g., Gibbs sampling) may be used to draw samples from a posterior distribution given the data subset. Finally, the samples from each ma- chine are combined to form samples from the full posterior. This embarrassingly parallel algorithm allows each machine to act inde- pendently on a subset of the data (without communication) until the final combination stage. We prove that our algorithm generates asymptotically exact samples and empirically demonstrate its ability to parallelize burn-in and sampling in several models. %Z Reissued by PMLR on 04 October 2026.
APA
ICT, Y.L., ICT, Y.Z., ICT, J.G., ICT, S.N. & ICT, X.C.. (2014). Position-Aware ListMLE: A Sequential Learning Process for Ranking. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:529-538 Available from https://proceedings.mlr.press/r12/ict14a.html. Reissued by PMLR on 04 October 2026.

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