An adaptive algorithm for selecting profitable keywords for search-based advertising services
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Dynamic cost-per-action mechanisms and applications to online advertising
Proceedings of the 17th international conference on World Wide Web
Multi-armed bandits in metric spaces
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Sharp dichotomies for regret minimization in metric spaces
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Sorting and selection on dynamic data
Theoretical Computer Science
The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery
INFORMS Journal on Computing
Incentive design for adaptive agents
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Hierarchical Knowledge Gradient for Sequential Sampling
The Journal of Machine Learning Research
Proceedings of the 3rd Innovations in Theoretical Computer Science Conference
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and incentives in user-generated content: multi-armed bandits with endogenous arms
Proceedings of the 4th conference on Innovations in Theoretical Computer Science
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In an online decision problem, an algorithm performs a sequence of trials, each of which involves selecting one element from a fixed set of alternatives (the "strategy set") whose costs vary over time. After T trials, the combined cost of the algorithm's choices is compared with that of the single strategy whose combined cost is minimum. Their difference is called regret, and one seeks algorithms which are efficient in that their regret is sublinear in T and polynomial in the problem size. We study an important class of online decision problems called generalized multi-armed bandit problems. In the past such problems have found applications in areas as diverse as statistics, computer science, economic theory, and medical decision-making. Most existing algorithms were efficient only in the case of a small (i.e. polynomial-sized) strategy set. We extend the theory by supplying non-trivial algorithms and lower bounds for cases in which the strategy set is much larger (exponential or infinite) and the cost function class is structured, e.g. by constraining the cost functions to be linear or convex. As applications, we consider adaptive routing in networks, adaptive pricing in electronic markets, and collaborative decision-making by untrusting peers in a dynamic environment. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)