Statistics Seminar

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TITLE: On the power and limits of adaptivity for sparse signal acquisition

SPEAKER: Mark Davenport


In this talk I will focus on the problem of recovering a sparse vector from a small number of noisy measurements. To begin, I will consider the case where the measurements are acquired in a nonadaptive fashion. I will establish a lower bound on the minimax mean-squared error of the recovered vector which very nearly matches the performance of â„“1-minimization techniques, and hence shows that these techniques are essentially optimal. I will then consider the case where the measurements are acquired sequentially in an adaptive manner. I will prove a lower bound that shows that, surprisingly, adaptivity does not allow for substantial improvement over standard nonadaptive techniques in terms of the minimax MSE. Nonetheless, I will also show that there are important regimes where the benefits of adaptive sensing are clear and overwhelming.


  • Workflow Status: Published
  • Created By: Anita Race
  • Created: 10/10/2014
  • Modified By: Fletcher Moore
  • Modified: 04/13/2017


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