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- W3021880962 abstract "We study an informative path planning problem where the goal is to minimize the time required to learn a spatial field using Gaussian Process (GP) regression. Specifically, given parameters (0< epsilon , delta < 1), our goal is to ensure that the predicted value at all points in an environment lies within (pm epsilon ) of the true value with probability at least (delta ). We study two versions of the problem. In the sensor placement version, the objective is to minimize the number of sensors placed. In the mobile sensing version, the objective is to minimize the total travel time required to visit the sensing locations. The total time is given by the time spent obtaining measurements as well as time to travel between measurement locations. By exploiting the smoothness properties of GP regression, we present constant-factor approximation algorithms for both problems that make accurate predictions at each point. Our algorithm is a deterministic, non-adaptive one and based on the Traveling Salesperson Problem. In addition to theoretical results, we also compare the empirical performance using a real-world dataset with other baseline strategies." @default.
- W3021880962 created "2020-05-13" @default.
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- W3021880962 date "2020-01-01" @default.
- W3021880962 modified "2023-09-25" @default.
- W3021880962 title "Learning a Spatial Field with Gaussian Process Regression in Minimum Time" @default.
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- W3021880962 doi "https://doi.org/10.1007/978-3-030-44051-0_18" @default.
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