The adwords problem: online keyword matching with budgeted bidders under random permutations

By Nikhil R. Devanur, T. Hayes. ACM Conference on Economics and Computation, 2009

413 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Marketing

View Paper: https://www.semanticscholar.org/paper/4b69669db26d1fbcbf54dfc6d1a35d48b58bc34f

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Citations in the corpus

A Dynamic Near-Optimal Algorithm for Online Linear Programming (2009, methodology): “We may point out that we have eliminated the condition on OPT obtained by [7] and only have a condition on bi which may be checkable before the execution, a property which is not provided by the condition of former type.”
Bandits with Knapsacks (2013, background): “3: Over the next | X | rounds, for each x ∈ X : choose an arm as an independent sample from D .”
Online Stochastic Matching: Beating 1-1/e (2009, neutral): “We leave this as an open question.”
Online Matching and Ad Allocation (2013, methodology): “” Note that for a fixed distribution, the algorithm of [35] (and the generalizations in [6, 44]) provides precisely such a guarantee.”
Optimal real-time bidding for display advertising (2014, background): “The objective function could be diverted to optimise the overall revenue for the search engine [2, 33, 14, 43], rather than the performance of each individual advertiser’s campaigns.”
Introduction to Multi-Armed Bandits (2019, background): “Third, estimating an optimal “dual” vector from samples and using this vector to guide subsequent “primal” decisions is a running theme in the work on stochastic packing problems (Devanur and Hayes, 2009; Agrawal et al., 2014; Devanur et al., 2019; Feldman et al., 2010; Molinaro and Ravi, 2012).”
Online Task Assignment in Crowdsourcing Markets (2012, methodology): “Exploiting this similarity, we designed the Dual Task Assigner algorithm by modifying the algorithm of Devanur and Hayes (2009) to handle and learn unknown parameters, and showed that DTA is competitive with respect to the of-ﬂine optimal when the number of worker arrivals is large.”