Throttling Poisson Processes

Part of Advances in Neural Information Processing Systems 23 (NIPS 2010)

Bibtex Metadata Paper


Uwe Dick, Peter Haider, Thomas Vanck, Michael Brückner, Tobias Scheffer


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.