Active Learning for Level Set Estimation
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 1344-1349.
Abstract
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.
@inproceedings{ijcai2013p202,
title = {Active Learning for Level Set Estimation},
author = {Gotovos, Alkis and Casati, Nathalie and Hitz, Gregory and Krause, Andreas},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {1344--1349},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/202},
}
TY - CPAPER TI - Active Learning for Level Set Estimation AU - Gotovos, Alkis AU - Casati, Nathalie AU - Hitz, Gregory AU - Krause, Andreas T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 1344 EP - 1349 UR - https://ijcai.org/proceedings/2013/202 ER -Copied
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