An optimal algorithm for on-line bipartite matching
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
AdWords and generalized online matching
Journal of the ACM (JACM)
The adwords problem: online keyword matching with budgeted bidders under random permutations
Proceedings of the 10th ACM conference on Electronic commerce
Bidding for Representative Allocations for Display Advertising
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Sampling bounds for stochastic optimization
APPROX'05/RANDOM'05 Proceedings of the 8th international workshop on Approximation, Randomization and Combinatorial Optimization Problems, and Proceedings of the 9th international conference on Randamization and Computation: algorithms and techniques
Online stochastic packing applied to display ad allocation
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Selective call out and real time bidding
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Near optimal online algorithms and fast approximation algorithms for resource allocation problems
Proceedings of the 12th ACM conference on Electronic commerce
Click shaping to optimize multiple objectives
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Simultaneous approximations for adversarial and stochastic online budgeted allocation
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Handling forecast errors while bidding for display advertising
Proceedings of the 21st international conference on World Wide Web
Online stochastic weighted matching: improved approximation algorithms
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
Traffic shaping to optimize ad delivery
Proceedings of the 13th ACM Conference on Electronic Commerce
Ad serving using a compact allocation plan
Proceedings of the 13th ACM Conference on Electronic Commerce
SHALE: an efficient algorithm for allocation of guaranteed display advertising
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Online allocation of display ads with smooth delivery
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Personalized click shaping through lagrangian duality for online recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
A unified optimization framework for auction and guaranteed delivery in online advertising
Proceedings of the 21st ACM international conference on Information and knowledge management
Optimizing budget constrained spend in search advertising
Proceedings of the sixth ACM international conference on Web search and data mining
Whole-page optimization and submodular welfare maximization with online bidders
Proceedings of the fourteenth ACM conference on Electronic commerce
Real-time bidding for online advertising: measurement and analysis
Proceedings of the Seventh International Workshop on Data Mining for Online Advertising
Partner tiering in display advertising
Proceedings of the 7th ACM international conference on Web search and data mining
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Motivated by the allocation problem facing publishers in display advertising we formulate the online assignment with forecast problem, a version of the online allocation problem where the algorithm has access to random samples from the future set of arriving vertices. We provide a solution that allows us to serve Internet users in an online manner that is provably nearly optimal. Our technique applies to the forecast version of a large class of online assignment problems, such as online bipartite matching, allocation, and budgeted bidders, in which we wish to minimize the value of some convex objective function subject to a set of linear supply and demand constraints. Our solution utilizes a particular subspace of the dual space, allowing us to describe the optimal primal solution implicitly in space proportional to the demand side of the input graph. More importantly, it allows us to prove that representing the primal solution using such a compact allocation plan yields a robust online algorithm which makes near-optimal online decisions. Furthermore, unlike the primal solution, we show that the compact allocation plan produced by considering only a sampled version of the original problem generalizes to produce a near optimal solution on the full problem instance.