Adaptive Sensor Placement for Continuous Spaces

James Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David Leslie, Sattar Vakili, Enrique Munoz De Cote
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2385-2393, 2019.

Abstract

We consider the problem of adaptively placing sensors along an interval to detect stochastically-generated events. We present a new formulation of the problem as a continuum-armed bandit problem with feedback in the form of partial observations of realisations of an inhomogeneous Poisson process. We design a solution method by combining Thompson sampling with nonparametric inference via increasingly granular Bayesian histograms and derive an $\tilde{O}(T^{2/3})$ bound on the Bayesian regret in $T$ rounds. This is coupled with the design of an efficent optimisation approach to select actions in polynomial time. In simulations we demonstrate our approach to have substantially lower and less variable regret than competitor algorithms.

Cite this Paper

BibTeX
@InProceedings{pmlr-v97-grant19a, title = {Adaptive Sensor Placement for Continuous Spaces}, author = {Grant, James and Boukouvalas, Alexis and Griffiths, Ryan-Rhys and Leslie, David and Vakili, Sattar and De Cote, Enrique Munoz}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, pages = {2385--2393}, year = {2019}, editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan}, volume = {97}, series = {Proceedings of Machine Learning Research}, month = {09--15 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v97/grant19a/grant19a.pdf}, url = {https://proceedings.mlr.press/v97/grant19a.html}, abstract = {We consider the problem of adaptively placing sensors along an interval to detect stochastically-generated events. We present a new formulation of the problem as a continuum-armed bandit problem with feedback in the form of partial observations of realisations of an inhomogeneous Poisson process. We design a solution method by combining Thompson sampling with nonparametric inference via increasingly granular Bayesian histograms and derive an $\tilde{O}(T^{2/3})$ bound on the Bayesian regret in $T$ rounds. This is coupled with the design of an efficent optimisation approach to select actions in polynomial time. In simulations we demonstrate our approach to have substantially lower and less variable regret than competitor algorithms.} }
Endnote
%0 Conference Paper %T Adaptive Sensor Placement for Continuous Spaces %A James Grant %A Alexis Boukouvalas %A Ryan-Rhys Griffiths %A David Leslie %A Sattar Vakili %A Enrique Munoz De Cote %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-grant19a %I PMLR %P 2385--2393 %U https://proceedings.mlr.press/v97/grant19a.html %V 97 %X We consider the problem of adaptively placing sensors along an interval to detect stochastically-generated events. We present a new formulation of the problem as a continuum-armed bandit problem with feedback in the form of partial observations of realisations of an inhomogeneous Poisson process. We design a solution method by combining Thompson sampling with nonparametric inference via increasingly granular Bayesian histograms and derive an $\tilde{O}(T^{2/3})$ bound on the Bayesian regret in $T$ rounds. This is coupled with the design of an efficent optimisation approach to select actions in polynomial time. In simulations we demonstrate our approach to have substantially lower and less variable regret than competitor algorithms.
APA
Grant, J., Boukouvalas, A., Griffiths, R., Leslie, D., Vakili, S. & De Cote, E.M.. (2019). Adaptive Sensor Placement for Continuous Spaces. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:2385-2393 Available from https://proceedings.mlr.press/v97/grant19a.html.

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