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Paper IDE.5.6
Paper Title Testing for Anomalies: Active Strategies and Non-asymptotic Analysis
Authors Dhruva Kartik, Ashutosh Nayyar, Urbashi Mitra, University of Southern California, United States
Session E.5: Hypothesis Testing I
Presentation Lecture
Track Detection and Estimation
Manuscript  Click here to download the manuscript
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Abstract The problem of verifying whether a multi-component system has anomalies or not is addressed. Each component can be probed over time in a data-driven manner to obtain noisy observations that indicate whether the selected component is anomalous or not. The aim is to minimize the probability of incorrectly declaring the system to be free of anomalies while ensuring that the probability of correctly declaring it to be safe is sufficiently large. This problem is modeled as an active hypothesis testing problem in the Neyman-Pearson setting. Component-selection and inference strategies are designed and analyzed in the non-asymptotic regime. For a specific class of homogeneous problems, stronger (with respect to prior work) non-asymptotic converse and achievability bounds are provided.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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