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June 2019

Ruihao Zhu — Hedging the Drift: Learning to Optimize under Non-Stationarity

June 21 @ 3:00 pm - 4:00 pm
George B. Dantzig Auditorium – 1174 Etcheverry Hall, 1174 Etcheverry Hall
Berkeley, CA 94720 United States
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Abstract: We introduce general data-driven decision-making algorithms that achieve state-of-the-art dynamic regret bounds for non-stationary bandit settings. They capture applications such as advertisement allocation and dynamic pricing in changing environments. We show how the difficulty posed by the (unknown 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 that first converts the rate-optimal Upper-Confidence-Bound (UCB) algorithm for stationary bandit settings into…

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