Adaptive Online Learning

NeurIPS 2015 Dylan J. FosterAlexander RakhlinKarthik Sridharan

We propose a general framework for studying adaptive regret bounds in the online learning framework, including model selection bounds and data-dependent bounds. Given a data- or model-dependent bound we ask, "Does there exist some algorithm achieving this bound?".. (read more)

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