Learning to Optimize under Non-Stationarity

6 Oct 2018Wang Chi CheungDavid Simchi-LeviRuihao Zhu

We introduce algorithms that achieve state-of-the-art \emph{dynamic regret} bounds for non-stationary linear stochastic bandit setting. It captures natural applications such as dynamic pricing and ads allocation in a changing environment... (read more)

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