A Tractable Probabilistic Model for Subset Selection

Yujia Shen, Arthur Choi, Adnan Darwiche
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:51-60, 2017.

Abstract

Subset selection tasks, such as top-k ranking, induce datasets where examples have cardinal- ities that are known a priori. In this paper, we propose a tractable probabilistic model for sub- set selection and show how it can be learned from data. Our proposed model is interpretable and subsumes a previously introduced model based on logistic regression. We show how the parameters of our model can be estimated in closed form given complete data, and propose an algorithm for learning its structure in an in- terpretable space. We highlight the intuitive structures that we learn via case studies. We finally show how our proposed model can be viewed as an instance of the recently proposed Probabilistic Sentential Decision Diagram.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-shen17a, title = {A Tractable Probabilistic Model for Subset Selection}, author = {Shen, Yujia and Choi, Arthur and Darwiche, Adnan}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {51--60}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/shen17a/shen17a.pdf}, url = {https://proceedings.mlr.press/r15/shen17a.html}, abstract = {Subset selection tasks, such as top-k ranking, induce datasets where examples have cardinal- ities that are known a priori. In this paper, we propose a tractable probabilistic model for sub- set selection and show how it can be learned from data. Our proposed model is interpretable and subsumes a previously introduced model based on logistic regression. We show how the parameters of our model can be estimated in closed form given complete data, and propose an algorithm for learning its structure in an in- terpretable space. We highlight the intuitive structures that we learn via case studies. We finally show how our proposed model can be viewed as an instance of the recently proposed Probabilistic Sentential Decision Diagram.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T A Tractable Probabilistic Model for Subset Selection %A Yujia Shen %A Arthur Choi %A Adnan Darwiche %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-shen17a %I PMLR %P 51--60 %U https://proceedings.mlr.press/r15/shen17a.html %V R15 %X Subset selection tasks, such as top-k ranking, induce datasets where examples have cardinal- ities that are known a priori. In this paper, we propose a tractable probabilistic model for sub- set selection and show how it can be learned from data. Our proposed model is interpretable and subsumes a previously introduced model based on logistic regression. We show how the parameters of our model can be estimated in closed form given complete data, and propose an algorithm for learning its structure in an in- terpretable space. We highlight the intuitive structures that we learn via case studies. We finally show how our proposed model can be viewed as an instance of the recently proposed Probabilistic Sentential Decision Diagram. %Z Reissued by PMLR on 04 October 2026.
APA
Shen, Y., Choi, A. & Darwiche, A.. (2017). A Tractable Probabilistic Model for Subset Selection. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:51-60 Available from https://proceedings.mlr.press/r15/shen17a.html. Reissued by PMLR on 04 October 2026.

Related Material