Adaptive Online Learning

@inproceedings{Foster2015AdaptiveOL,
  title={Adaptive Online Learning},
  author={Dylan J. Foster and Alexander Rakhlin and Karthik Sridharan},
  booktitle={NIPS},
  year={2015}
}
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 dataor model-dependent bound we ask, “Does there exist some algorithm achieving this bound?” We show that modifications to recently introduced sequential complexity measures can be used to answer this question by providing sufficient conditions under which adaptive rates can be achieved. In particular each adaptive rate induces… CONTINUE READING
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