Active learning for adaptive mobile sensing networks
Proceedings of the 5th international conference on Information processing in sensor networks
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
ACE in the Hole: Adaptive Contour Estimation Using Collaborating Mobile Sensors
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Actively learning level-sets of composite functions
Proceedings of the 25th international conference on Machine learning
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Many information gathering problems require determining the set of points, for which an unknown function takes value above or below some given threshold level. We formalize this task as a classification problem with sequential measurements, where the unknown function is modeled as a sample from a Gaussian process (GP). We propose LSE, an algorithm that guides both sampling and classification based on GP-derived confidence bounds, and provide theoretical guarantees about its sample complexity. Furthermore, we extend LSE and its theory to two more natural settings: (1) where the threshold level is implicitly defined as a percentage of the (unknown) maximum of the target function and (2) where samples are selected in batches. We evaluate the effectiveness of our proposed methods on two problems of practical interest, namely autonomous monitoring of algal populations in a lake environment and geolocating network latency.