The weighted majority algorithm
Information and Computation
Game theory, on-line prediction and boosting
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Journal of the ACM (JACM)
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Competitive analysis of incentive compatible on-line auctions
Proceedings of the 2nd ACM conference on Electronic commerce
Competitive auctions and digital goods
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Competitive generalized auctions
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Incentive-compatible online auctions for digital goods
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
Online learning in online auctions
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Gambling in a rigged casino: The adversarial multi-armed bandit problem
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Anytime algorithms for multi-armed bandit problems
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Online algorithms for market clearing
Journal of the ACM (JACM)
Regret Minimization Under Partial Monitoring
Mathematics of Operations Research
Dynamic pricing for impatient bidders
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
On Fixed Convex Combinations of No-Regret Learners
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Dynamic pricing for impatient bidders
ACM Transactions on Algorithms (TALG)
Multi-parameter mechanism design and sequential posted pricing
Proceedings of the forty-second ACM symposium on Theory of computing
On the competitive ratio of online sampling auctions
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Learning the demand curve in posted-price digital goods auctions
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Frequency capping in online advertising
WADS'11 Proceedings of the 12th international conference on Algorithms and data structures
On the Competitive Ratio of Online Sampling Auctions
ACM Transactions on Economics and Computation - Special Issue on Algorithmic Game Theory
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We consider the problem of revenue maximization in online auctions, that is, auctions in which bids are received and dealt with one-by-one. In this paper, we demonstrate that results from online learning can be usefully applied in this context, and we derive a new auction for digital goods that achieves a constant competitive ratio with respect to the optimal (offline) fixed price revenue. This substantially improves upon the best previously known competitive ratio for this problem of O(exp(√log log h)). We also apply our techniques to the related problem of designing online posted price mechanisms, in which the seller declares a price for each of a series of buyers, and each buyer either accepts or rejects the good at that price. Despite the relative lack of information in this setting, we show that online learning techniques can be used to obtain results for online posted price mechanisms which are similar to those obtained for online auctions.