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Throttling Poisson Processes
Uwe Dick · Peter Haider · Tobias Scheffer

Wed Dec 08 11:10 AM -- 11:15 AM (PST) @ Regency Ballroom

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.

Author Information

Uwe Dick (University of Potsdam)
Peter Haider (University of Potsdam)
Tobias Scheffer (Universität Potsdam)

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