A Robust Approach to Sequential Information Theoretic Planning

Sue Zheng, Jason Pacheco, John Fisher
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:5941-5949, 2018.

Abstract

In many sequential planning applications a natural approach to generating high quality plans is to maximize an information reward such as mutual information (MI). Unfortunately, MI lacks a closed form in all but trivial models, and so must be estimated. In applications where the cost of plan execution is expensive, one desires planning estimates which admit theoretical guarantees. Through the use of robust M-estimators we obtain bounds on absolute deviation of estimated MI. Moreover, we propose a sequential algorithm which integrates inference and planning by maximally reusing particles in each stage. We validate the utility of using robust estimators in the sequential approach on a Gaussian Markov Random Field wherein information measures have a closed form. Lastly, we demonstrate the benefits of our integrated approach in the context of sequential experiment design for inferring causal regulatory networks from gene expression levels. Our method shows improvements over a recent method which selects intervention experiments based on the same MI objective.

Cite this Paper

BibTeX
@InProceedings{pmlr-v80-zheng18b, title = {A Robust Approach to Sequential Information Theoretic Planning}, author = {Zheng, Sue and Pacheco, Jason and Fisher, John}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {5941--5949}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/zheng18b/zheng18b.pdf}, url = {https://proceedings.mlr.press/v80/zheng18b.html}, abstract = {In many sequential planning applications a natural approach to generating high quality plans is to maximize an information reward such as mutual information (MI). Unfortunately, MI lacks a closed form in all but trivial models, and so must be estimated. In applications where the cost of plan execution is expensive, one desires planning estimates which admit theoretical guarantees. Through the use of robust M-estimators we obtain bounds on absolute deviation of estimated MI. Moreover, we propose a sequential algorithm which integrates inference and planning by maximally reusing particles in each stage. We validate the utility of using robust estimators in the sequential approach on a Gaussian Markov Random Field wherein information measures have a closed form. Lastly, we demonstrate the benefits of our integrated approach in the context of sequential experiment design for inferring causal regulatory networks from gene expression levels. Our method shows improvements over a recent method which selects intervention experiments based on the same MI objective.} }
Endnote
%0 Conference Paper %T A Robust Approach to Sequential Information Theoretic Planning %A Sue Zheng %A Jason Pacheco %A John Fisher %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-zheng18b %I PMLR %P 5941--5949 %U https://proceedings.mlr.press/v80/zheng18b.html %V 80 %X In many sequential planning applications a natural approach to generating high quality plans is to maximize an information reward such as mutual information (MI). Unfortunately, MI lacks a closed form in all but trivial models, and so must be estimated. In applications where the cost of plan execution is expensive, one desires planning estimates which admit theoretical guarantees. Through the use of robust M-estimators we obtain bounds on absolute deviation of estimated MI. Moreover, we propose a sequential algorithm which integrates inference and planning by maximally reusing particles in each stage. We validate the utility of using robust estimators in the sequential approach on a Gaussian Markov Random Field wherein information measures have a closed form. Lastly, we demonstrate the benefits of our integrated approach in the context of sequential experiment design for inferring causal regulatory networks from gene expression levels. Our method shows improvements over a recent method which selects intervention experiments based on the same MI objective.
APA
Zheng, S., Pacheco, J. & Fisher, J.. (2018). A Robust Approach to Sequential Information Theoretic Planning. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research 80:5941-5949 Available from https://proceedings.mlr.press/v80/zheng18b.html.

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