Bandit Convex Optimization in Non-stationary Environments

AISTATS, pp. 1508-1518, 2019.

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Abstract:

Bandit Convex Optimization (BCO) is a fundamental framework for modeling sequential decision-making with partial information, where the only feedback available to the player is the one-point or two-point function values. In this paper, we investigate BCO in non-stationary environments and choose the \emph{dynamic regret} as the performa...More

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