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• W2920202034 abstract "We introduce data-driven decision-making algorithms that achieve state-of-the-art emph{dynamic regret} bounds for non-stationary bandit settings. These settings capture applications such as advertisement allocation, dynamic pricing, and traffic network routing in changing environments. We show how the difficulty posed by the (unknown emph{a priori} and possibly adversarial) non-stationarity can be overcome by an unconventional marriage between stochastic and adversarial bandit learning algorithms. Our main contribution is a general algorithmic recipe for a wide variety of non-stationary bandit problems. Specifically, we design and analyze the sliding window-upper confidence bound algorithm that achieves the optimal dynamic regret bound for each of the settings when we know the respective underlying emph{variation budget}, which quantifies the total amount of temporal variation of the latent environments. Boosted by the novel bandit-over-bandit framework that adapts to the latent changes, we can further enjoy the (nearly) optimal dynamic regret bounds in a (surprisingly) parameter-free manner. In addition to the classical exploration-exploitation trade-off, our algorithms leverage the power of the forgetting principle in the learning processes, which is vital in changing environments. Our extensive numerical experiments on both synthetic and real world online auto-loan datasets show that our proposed algorithms achieve superior empirical performance compared to existing algorithms." @default.
• W2920202034 created "2019-03-11" @default.
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• W2920202034 date "2019-03-04" @default.
• W2920202034 modified "2023-09-26" @default.
• W2920202034 title "Hedging the Drift: Learning to Optimize under Non-Stationarity" @default.
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• W2920202034 doi "https://doi.org/10.48550/arxiv.1903.01461" @default.
• W2920202034 hasPublicationYear "2019" @default.
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