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SSG Seminar Abstract

Distributed Learning in Sensor Networks

Sanjeev Kulkarni
Princeton University

One of the key challenges for sensor networks is learning and high-level decision-making. These tasks must be accomplished in a distributed setting and in the face of scarce resources (time, bandwidth, and power). This talk describes some of our recent work in this area. Specifically, we describe some results on a problem of aggregating probability forecasts and use a similar approach for learning in a distributed setting that combines ideas from kernel methods and graphical models.

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