# Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces

arXiv: https://arxiv.org/abs/2303.00028 · HTML: https://mlsanity.com/paper/2303.00028/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 80 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Gaussian Processes for Big Data](https://mlsanity.com/paper/1309.6835/) (2013)
- [Understanding Probabilistic Sparse Gaussian Process Approximations](https://mlsanity.com/paper/1606.04820/) (2016)
- [Rates of Convergence for Sparse Variational Gaussian Process Regression](https://mlsanity.com/paper/1903.03571/) (2019)
- [On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes](https://mlsanity.com/paper/1504.07027/) (2015)
- [Convergence of Sparse Variational Inference in Gaussian Processes Regression](https://mlsanity.com/paper/2008.00323/) (2020)
- [A Framework for Interdomain and Multioutput Gaussian Processes](https://mlsanity.com/paper/2003.01115/) (2020)
- [Linearly constrained Gaussian processes](https://mlsanity.com/paper/1703.00787/) (2017)
- [Spatio-Temporal Variational Gaussian Processes](https://mlsanity.com/paper/2111.01732/) (2021)
- [Actively Learning Gaussian Process Dynamics](https://mlsanity.com/paper/1911.09946/) (2019)
- [apricot: Submodular selection for data summarization in Python](https://mlsanity.com/paper/1906.03543/) (2019)
- [Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees](https://mlsanity.com/paper/2111.01721/) (2021)
- [Informative Planning and Online Learning with Sparse Gaussian Processes](https://mlsanity.com/paper/1609.07560/) (2016)
- [Neural Non-Stationary Spectral Kernel](https://mlsanity.com/paper/1811.10978/) (2018)
- [Learning a Spatial Field in Minimum Time with a Team of Robots](https://mlsanity.com/paper/1909.01895/) (2019)
- [Online Informative Path Planning for Active Information Gathering of a 3D Surface](https://mlsanity.com/paper/2103.09556/) (2021)
- [Occupancy Map Building through Bayesian Exploration](https://mlsanity.com/paper/1703.00227/) (2017)
- [Approximate Computational Approaches for Bayesian Sensor Placement in High Dimensions](https://mlsanity.com/paper/1703.00368/) (2017)

## Built on by (most cited first, 1 of 1)

- [Designing an Optimal Sensor Network via Minimizing Information Loss](https://mlsanity.com/paper/2512.05940/) (2025)

## Abstract (arXiv)

The sensor placement problem is a common problem that arises when monitoring correlated phenomena, such as temperature, precipitation, and salinity. Existing approaches to this problem typically formulate it as the maximization of information metrics, such as mutual information~(MI), and use optimization methods such as greedy algorithms in discrete domains, and derivative-free optimization methods such as genetic algorithms in continuous domains. However, computing MI for sensor placement requires discretizing the environment, and its computation cost depends on the size of the discretized environment. These limitations restrict these approaches from scaling to large problems. We present a novel formulation to the SP problem based on variational approximation that can be optimized using gradient descent, allowing us to efficiently find solutions in continuous domains. We generalize our method to also handle discrete environments. Our experimental results on four real-world datasets demonstrate that our approach generates sensor placements consistently on par with or better than the prior state-of-the-art approaches in terms of both MI and reconstruction quality, all while being significantly faster. Our computationally efficient approach enables both large-scale sensor placement and fast robotic sensor placement for informative path planning algorithms.