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Cycles in adversarial regularized learning
[article]
2017
arXiv
pre-print
Regularized learning is a fundamental technique in online optimization, machine learning and many other fields of computer science. A natural question that arises in these settings is how regularized learning algorithms behave when faced against each other. We study a natural formulation of this problem by coupling regularized learning dynamics in zero-sum games. We show that the system's behavior is Poincar\'e recurrent, implying that almost every trajectory revisits any (arbitrarily small)
arXiv:1709.02738v1
fatcat:dimq2dcy25dp7boai34w7wevly