2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-45.6
Paper Title AN ORDER-OPTIMAL ADAPTIVE TEST PLAN FOR NOISY GROUP TESTING UNDER UNKNOWN NOISE MODELS
Authors Sudeep Salgia, Qing Zhao, Cornell University, United States
SessionMLSP-45: Performance Bounds
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-SLER] Sequential learning; sequential decision methods
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract We consider the problem of noisy group testing where the test results are corrupted by noise with an unknown distribution. We propose an adaptive test plan consisting of a hierarchy of biased random walks guided by a local sequential test which together lend adaptivity and agnosticism to the unknown noise model. We show that the proposed test plan is order optimal in both the population size and the error rate.