Throttling Poisson Processes

author: Uwe Dick, Department RG2: Machine Learning, Max Planck Institute for Computer Science, Max Planck Institute
published: March 25, 2011,   recorded: December 2010,   views: 2834


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We study a setting in which Poisson processes generate sequences of decision-making events. The optimization goal is allowed to depend on the rate of decision outcomes; the rate may depend on a potentially long backlog of events and decisions. We model the problem as a Poisson process with a throttling policy that enforces a data-dependent rate limit and reduce the learning problem to a convex optimization problem that can be solved efficiently. This problem setting matches applications in which damage caused by an attacker grows as a function of the rate of unsuppressed hostile events. We report on experiments on abuse detection for an email service.

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