Allerton 2015 Paper Abstract


Paper ThC5.4

Lipor, John (University of Michigan), Balzano, Laura (University of Michigan), Kerkez, Branko (University of Michigan), Scavia, Don (University of Michigan)

Quantile Search: A Distance-Penalized Active Learning Algorithm for Spatial Sampling

Scheduled for presentation during the Regular Session "Detection and Estimation" (ThC5), Thursday, October 1, 2015, 14:30−14:50, Lower Level

53rd Annual Allerton Conference on Communication, Control, and Computing, Sept 29-Oct 2, 2015, Allerton Park and Retreat Center, Monticello, IL, USA

This information is tentative and subject to change. Compiled on December 5, 2021

Keywords Universal Algorithms and Machine Learning, Detection and Estimation


Adaptive sampling theory has shown that, with proper assumptions on the signal class, algorithms exist to reconstruct a signal in Rd with an optimal number of samples. We generalize this problem to when the cost of sampling is not only the number of samples but also the distance traveled between samples. This is motivated by our work studying regions of low oxygen concentration in the Great Lakes. We show that for one-dimensional threshold classifiers, a tradeoff between number of samples and distance traveled can be achieved using a generalization of binary search, which we refer to as quantile search. We derive the expected total sampling time for noiseless measurements and the expected number of samples for an extension to the noisy case. We illustrate our results in simulations relevant to our sampling application.



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