An optimal algorithm for on-line bipartite matching
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An introduction to computational learning theory
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AdWords and Generalized On-line Matching
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The adwords problem: online keyword matching with budgeted bidders under random permutations
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FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
Online primal-dual algorithms for maximizing ad-auctions revenue
ESA'07 Proceedings of the 15th annual European conference on Algorithms
Fast algorithms for finding matchings in lopsided bipartite graphs with applications to display ads
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Online bipartite matching with unknown distributions
Proceedings of the forty-third annual ACM symposium on Theory of computing
Online bipartite matching with random arrivals: an approach based on strongly factor-revealing LPs
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Online stochastic matching: online actions based on offline statistics
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The design and analysis of online algorithms, where the input to the algorithm is revealed over time and the algorithm has to make decisions immediately without knowing the future input, has received a revived interest in the last few years primarily due to their application to online advertising. The canonical problem is the Adwords problem, which is motivated by the problem of optimally allocating ad slots on search queries to budget constrained advertisers. It involves simplifications that ignore certain aspects of the actual way this allocation is done. For instance, it assumes a "first-price" pay-per-impression scheme, ignoring the game theoretic aspects, and considers only one slot per query. To be precise, the Adwords problem is as follows.