| Document Date: 2012-12-03 21:04:12
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Company Microsoft / / Country United States / / / Facility stable ERM / / IndustryTerm Online Linear Programming / respective algorithms / proximal type algorithms / bounded regret algorithm / online learning method / online case / canonical learning algorithm / Online learning / online learning framework / dot product / online learning setup / online learning algorithms / online learning conditions / mirror descent algorithms / online setting / online stability / online learnability / stable algorithm / arbitrary online learning algorithms / online adversarial setting / namely online stability / stable online learning algorithms / online stable algorithm / mirror descent algorithm / online learning setting / online stable / online algorithm / intuitive algorithm / online learning algorithm / online algorithms / online learning game / online stability condition / learning algorithms / / Organization University of Michigan / Online Learning Ankan Saha Department / University of Chicago / / Person Forward Regret / / Position Follow-The-Leader / Follow The Leader / player / Follow-The-Regularized-Leader / Follow The Regularized Leader / / Product Latte W10 Portable Audio Device / Cowon D2+ Portable Audio Device / / ProgrammingLanguage C / L / / ProvinceOrState Michigan / / Technology stable algorithm / online algorithm / three algorithms / online stable algorithm / alternative stable algorithm / existing OCP algorithms / canonical learning algorithm / FTL algorithm / FOBOS mirror descent algorithm / existing online algorithms / arbitrary online learning algorithms / two algorithms / bounded regret algorithm / online learning algorithm / online algorithms / proximal type algorithms / respective algorithms / stable online learning algorithms / online learning algorithms / mirror descent algorithms / FTRL algorithms / intuitive algorithm / /
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The Interplay Between Stability and Regret in Online Learning Ankan Saha Department of Computer Science University of Chicago