# Online Learning for Adversaries with Memory: Price of Past Mistakes

## Author(s): Anava, Oren; Hazan, Elad; Mannor, Shie

To refer to this page use: http://arks.princeton.edu/ark:/88435/pr18g31
DC FieldValueLanguage
dc.contributor.authorAnava, Oren-
dc.contributor.authorMannor, Shie-
dc.date.accessioned2021-10-08T19:49:36Z-
dc.date.available2021-10-08T19:49:36Z-
dc.date.issued2015en_US
dc.identifier.citationAnava, Oren, Elad Hazan, and Shie Mannor. "Online Learning for Adversaries with Memory: Price of Past Mistakes." In Advances in Neural Information Processing Systems 28 (2015).en_US
dc.identifier.urihttps://papers.nips.cc/paper/2015/file/38913e1d6a7b94cb0f55994f679f5956-Paper.pdf-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr18g31-
dc.description.abstractThe framework of online learning with memory naturally captures learning problems with temporal effects, and was previously studied for the experts setting. In this work we extend the notion of learning with memory to the general Online Convex Optimization (OCO) framework, and present two algorithms that attain low regret. The first algorithm applies to Lipschitz continuous loss functions, obtaining optimal regret bounds for both convex and strongly convex losses. The second algorithm attains the optimal regret bounds and applies more broadly to convex losses without requiring Lipschitz continuity, yet is more complicated to implement. We complement the theoretic results with two applications: statistical arbitrage in finance, and multi-step ahead prediction in statistics.en_US
dc.language.isoen_USen_US
dc.relation.ispartofAdvances in Neural Information Processing Systemsen_US
dc.rightsFinal published version. Article is made available in OAR by the publisher's permission or policy.en_US
dc.titleOnline Learning for Adversaries with Memory: Price of Past Mistakesen_US
dc.typeConference Articleen_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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