The weighted majority algorithm
Information and Computation
Journal of the ACM (JACM)
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
Incentive-compatible online auctions for digital goods
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
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
Beyond VCG: Frugality of Truthful Mechanisms
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Auctions for structured procurement
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
A Knapsack Secretary Problem with Applications
APPROX '07/RANDOM '07 Proceedings of the 10th International Workshop on Approximation and the 11th International Workshop on Randomization, and Combinatorial Optimization. Algorithms and Techniques
Secretary problems: weights and discounts
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
Improved rates for the stochastic continuum-armed bandit problem
COLT'07 Proceedings of the 20th annual conference on Learning theory
The labor economics of paid crowdsourcing
Proceedings of the 11th ACM conference on Electronic commerce
Financial incentives and the "performance of crowds"
ACM SIGKDD Explorations Newsletter
FOCS '10 Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science
Designing incentives for inexpert human raters
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Proceedings of the fifth ACM international conference on Web search and data mining
Dynamic pricing with limited supply
Proceedings of the 13th ACM Conference on Electronic Commerce
Learning on a budget: posted price mechanisms for online procurement
Proceedings of the 13th ACM Conference on Electronic Commerce
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What price should be offered to a worker for a task in an online labor market? How can one enable workers to express the amount they desire to receive for the task completion? Designing optimal pricing policies and determining the right monetary incentives is central to maximizing requester's utility and workers' profits. Yet, current crowdsourcing platforms only offer a limited capability to the requester in designing the pricing policies and often rules of thumb are used to price tasks. This limitation could result in inefficient use of the requester's budget or workers becoming disinterested in the task. In this paper, we address these questions and present mechanisms using the approach of regret minimization in online learning. We exploit a link between procurement auctions and multi-armed bandits to design mechanisms that are budget feasible, achieve near-optimal utility for the requester, are incentive compatible (truthful) for workers and make minimal assumptions about the distribution of workers' true costs. Our main contribution is a novel, no-regret posted price mechanism, BP-UCB, for budgeted procurement in stochastic online settings. We prove strong theoretical guarantees about our mechanism, and extensively evaluate it in simulations as well as on real data from the Mechanical Turk platform. Compared to the state of the art, our approach leads to a 180% increase in utility.