Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Learning in the presence of malicious errors
SIAM Journal on Computing
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Algorithmic mechanism design (extended abstract)
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Truth revelation in approximately efficient combinatorial auctions
Journal of the ACM (JACM)
Theoretical Computer Science
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Mechanism Design via Machine Learning
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Can machine learning be secure?
ASIACCS '06 Proceedings of the 2006 ACM Symposium on Information, computer and communications security
Truthful randomized mechanisms for combinatorial auctions
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Algorithmic Game Theory
Thirteen Reasons Why the Vickrey-Clarke-Groves Process Is Not Practical
Operations Research
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Towards a theory of incentives in machine learning
ACM SIGecom Exchanges
Good learners for evil teachers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Approximate mechanism design without money
Proceedings of the 10th ACM conference on Electronic commerce
Strategyproof classification under constant hypotheses: a tale of two functions
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Strategyproof classification with shared inputs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Tighter Bounds for Facility Games
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Competitive Repeated Allocation without Payments
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Asymptotically optimal strategy-proof mechanisms for two-facility games
Proceedings of the 11th ACM conference on Electronic commerce
On the limits of dictatorial classification
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Strategy-proof allocation of multiple items between two agents without payments or priors
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
On strategy-proof allocation without payments or priors
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
Crowd IQ: aggregating opinions to boost performance
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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We initiate the study of incentives in a general machine learning framework. We focus on a game-theoretic regression learning setting where private information is elicited from multiple agents with different, possibly conflicting, views on how to label the points of an input space. This conflict potentially gives rise to untruthfulness on the part of the agents. In the restricted but important case when every agent cares about a single point, and under mild assumptions, we show that agents are motivated to tell the truth. In a more general setting, we study the power and limitations of mechanisms without payments. We finally establish that, in the general setting, the VCG mechanism goes a long way in guaranteeing truthfulness and economic efficiency.