The Continuum-Armed Bandit Problem
SIAM Journal on Control and Optimization
Competitive analysis of incentive compatible on-line auctions
Proceedings of the 2nd ACM conference on Electronic commerce
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
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Online learning in online auctions
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
The Value of Knowing a Demand Curve: Bounds on Regret for Online Posted-Price Auctions
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Adaptive limited-supply online auctions
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Prediction, Learning, and Games
Prediction, Learning, and Games
Dynamic cost-per-action mechanisms and applications to online advertising
Proceedings of the 17th international conference on World Wide Web
Optimal mechanism design and money burning
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Multi-armed bandits in metric spaces
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
The ratio index for budgeted learning, with applications
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Limited and online supply and the bayesian foundations of prior-free mechanism design
Proceedings of the 10th ACM conference on Electronic commerce
Characterizing truthful multi-armed bandit mechanisms: extended abstract
Proceedings of the 10th ACM conference on Electronic commerce
The price of truthfulness for pay-per-click auctions
Proceedings of the 10th ACM conference on Electronic commerce
Improved rates for the stochastic continuum-armed bandit problem
COLT'07 Proceedings of the 20th annual conference on Learning theory
Multi-parameter mechanism design and sequential posted pricing
Proceedings of the forty-second ACM symposium on Theory of computing
Truthful mechanisms with implicit payment computation
Proceedings of the 11th ACM conference on Electronic commerce
Revenue maximization with a single sample
Proceedings of the 11th ACM conference on Electronic commerce
Pure exploration in multi-armed bandits problems
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
Approximation schemes for sequential posted pricing in multi-unit auctions
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Regret Bounds and Minimax Policies under Partial Monitoring
The Journal of Machine Learning Research
On the Minimax Complexity of Pricing in a Changing Environment
Operations Research
Mechanism design via correlation gap
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Truthful incentives in crowdsourcing tasks using regret minimization mechanisms
Proceedings of the 22nd international conference on World Wide Web
Real time bid optimization with smooth budget delivery in online advertising
Proceedings of the Seventh International Workshop on Data Mining for Online Advertising
Sequential decision making with vector outcomes
Proceedings of the 5th conference on Innovations in theoretical computer science
Online pricing for bundles of multiple items
Journal of Global Optimization
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We consider the problem of designing revenue maximizing online posted-price mechanisms when the seller has limited supply. A seller has k identical items for sale and is facing n potential buyers ("agents") that are arriving sequentially. Each agent is interested in buying one item. Each agent's value for an item is an independent sample from some fixed (but unknown) distribution with support [0,1]. The seller offers a take-it-or-leave-it price to each arriving agent (possibly different for different agents), and aims to maximize his expected revenue. We focus on mechanisms that do not use any information about the distribution; such mechanisms are called "detail-free" (an alternative term is "prior-independent"). They are desirable because knowing the distribution is unrealistic in many practical scenarios. We study how the revenue of such mechanisms compares to the revenue of the optimal offline mechanism that knows the distribution ("offline benchmark"). We present a detail-free online posted-price mechanism whose revenue is at most O((k log n)2/3) less than the offline benchmark, for every distribution that is regular. In fact, this guarantee holds without any assumptions if the benchmark is relaxed to fixed-price mechanisms. Further, we prove a matching lower bound. The performance guarantee for the same mechanism can be improved to O(k log n), with a distribution-dependent constant, if the ratio k/n is sufficiently small. We show that, in the worst case over all demand distributions, this is essentially the best rate that can be obtained with a distribution-specific constant.