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Decentralized hypothesis testing problems Martin Wainwright Department of Statistics Department of Electrical Engineering and Computer Sciences and Statistics UC Berkeley, California We consider the problem of testing a binary hypothesis in a decentralized setting. More concretely, the goal is to design compression rules at the sensors (which determine the messages that are relayed to the fusion center), as well as a decision function at the fusion center so as to minimize the overall probability of error. In contrast to most previous work (which focuses on the case when the underlying distributions have known parametric forms), we consider the problem of learning compression rules based on a set of empirical samples. We propose a computationally efficient technique based on regularized forms of of the empirical risk and kernel methods, and analyze its statistical properties. Part of the analysis is based on a connection between surrogates to the 0-1 loss, and the class of $f$-divergences (or Ali-Silvey distances). Joint work with XuanLong Nguyen and Michael Jordan, UC Berkeley |
www.stat.colostate.edu/graybillconference |