Vine copula structure learning via Monte Carlo tree search

Bo Chang, Shenyi Pan, Harry Joe
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:353-361, 2019.

Abstract

Monte Carlo tree search (MCTS) has been widely adopted in various game and planning problems. It can efficiently explore a search space with guided random sampling. In statistics, vine copulas are flexible multivariate dependence models that adopt vine structures, which are based on a hierarchy of trees to express conditional dependence, and bivariate copulas on the edges of the trees. The vine structure learning problem has been challenging due to the large search space. To tackle this problem, we propose a novel approach to learning vine structures using MCTS. The proposed method has significantly better performance over the existing methods under various experimental setups.

Cite this Paper


BibTeX
@InProceedings{pmlr-v89-chang19a, title = {Vine copula structure learning via Monte Carlo tree search}, author = {Chang, Bo and Pan, Shenyi and Joe, Harry}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {353--361}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/chang19a/chang19a.pdf}, url = {http://proceedings.mlr.press/v89/chang19a.html}, abstract = {Monte Carlo tree search (MCTS) has been widely adopted in various game and planning problems. It can efficiently explore a search space with guided random sampling. In statistics, vine copulas are flexible multivariate dependence models that adopt vine structures, which are based on a hierarchy of trees to express conditional dependence, and bivariate copulas on the edges of the trees. The vine structure learning problem has been challenging due to the large search space. To tackle this problem, we propose a novel approach to learning vine structures using MCTS. The proposed method has significantly better performance over the existing methods under various experimental setups.} }
Endnote
%0 Conference Paper %T Vine copula structure learning via Monte Carlo tree search %A Bo Chang %A Shenyi Pan %A Harry Joe %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-chang19a %I PMLR %P 353--361 %U http://proceedings.mlr.press/v89/chang19a.html %V 89 %X Monte Carlo tree search (MCTS) has been widely adopted in various game and planning problems. It can efficiently explore a search space with guided random sampling. In statistics, vine copulas are flexible multivariate dependence models that adopt vine structures, which are based on a hierarchy of trees to express conditional dependence, and bivariate copulas on the edges of the trees. The vine structure learning problem has been challenging due to the large search space. To tackle this problem, we propose a novel approach to learning vine structures using MCTS. The proposed method has significantly better performance over the existing methods under various experimental setups.
APA
Chang, B., Pan, S. & Joe, H.. (2019). Vine copula structure learning via Monte Carlo tree search. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:353-361 Available from http://proceedings.mlr.press/v89/chang19a.html.

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