# Adaptive Sensor Placement for Continuous Spaces

arXiv: https://arxiv.org/abs/1905.06821 · HTML: https://mlsanity.com/paper/1905.06821/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2019.

## Built on by (most cited first, 4 of 4)

- [High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning](https://mlsanity.com/paper/2106.03609/) (2021)
- [Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification](https://mlsanity.com/paper/2109.14412/) (2021)
- [Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion](https://mlsanity.com/paper/2111.12602/) (2021)
- [LOFA: Online Influence Maximization under Full-Bandit Feedback using Lazy Forward Selection](https://mlsanity.com/paper/2601.00933/) (2026)

## Abstract (arXiv)

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 tildeO(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.