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• W2751585789 abstract "We consider the problem of repeated bidding in online advertising auctions when some side information (e.g. browser cookies) is available ahead of submitting a bid in the form of a $d$-dimensional vector. The goal for the advertiser is to maximize the total utility (e.g. the total number of clicks) derived from displaying ads given that a limited budget $B$ is allocated for a given time horizon $T$. Optimizing the bids is modeled as a contextual Multi-Armed Bandit (MAB) problem with a knapsack constraint and a continuum of arms. We develop UCB-type algorithms that combine two streams of literature: the confidence-set approach to linear contextual MABs and the probabilistic bisection search method for stochastic root-finding. Under mild assumptions on the underlying unknown distribution, we establish distribution-independent regret bounds of order $tilde{O}(d cdot sqrt{T})$ when either $B = infty$ or when $B$ scales linearly with $T$." @default.
• W2751585789 created "2017-09-15" @default.
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• W2751585789 date "2017-01-01" @default.
• W2751585789 modified "2023-09-24" @default.
• W2751585789 title "Real-Time Bidding with Side Information" @default.
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