Grant, James and Boukouvalas, Alexis and Griffiths, Ryan-Rhys and Leslie, David and Vakili, Sattar and Munoz de Cote, Enrique (2019) Adaptive sensor placement for continuous spaces. In: Proceedings of the 36 th International Conference on Machine Learning, Long Beach, California, PMLR 97, 2019 :. Proceedings of Machine Learning Research . Proceedings of Machine Learning Research.
1905.06821.pdf - Accepted Version
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Abstract
We consider the problem of adaptively placing sensors along an interval to detect stochasticallygenerated 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 O˜(T2/3) 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.