Active Multi-Fidelity Bayesian Online Changepoint Detection

Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations

Abstract

Online algorithms for detecting changepoints, or abrupt shifts in the behavior of a time series, are often deployed with limited resources, e.g., to edge computing settings such as mobile phones or industrial sensors. In these scenarios it may be beneficial to trade the cost of collecting an environmental measurement against the quality or “fidelity” of this measurement and how the measurement affects changepoint estimation. For instance, one might decide between inertial measurements or GPS to determine changepoints for motion. A Bayesian approach to changepoint detection is particularly appealing because we can represent our posterior uncertainty about changepoints and make active, cost-sensitive decisions about data fidelity to reduce this posterior uncertainty. Moreover, the total cost could be dramatically lowered through active fidelity switching, while remaining robust to changes in data distribution. We propose a multi-fidelity approach that makes cost-sensitive decisions about which data fidelity to collect based on maximizing information gain with respect to changepoints. We evaluate this framework on synthetic, video, and audio data and show that this information-based approach results in accurate predictions while reducing total cost.

Original languageEnglish (US)
Pages1916-1926
Number of pages11
StatePublished - 2021
Event37th Conference on Uncertainty in Artificial Intelligence, UAI 2021 - Virtual, Online
Duration: Jul 27 2021Jul 30 2021

Conference

Conference37th Conference on Uncertainty in Artificial Intelligence, UAI 2021
CityVirtual, Online
Period7/27/217/30/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Applied Mathematics

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