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
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
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
Reducing mechanism design to algorithm design via machine learning
Journal of Computer and System Sciences
Thirteen Reasons Why the Vickrey-Clarke-Groves Process Is Not Practical
Operations Research
The learnability of voting rules
Artificial Intelligence
Inventory Management of a Fast-Fashion Retail Network
Operations Research
Proceedings of the 11th ACM conference on Electronic commerce
Sum of us: strategyproof selection from the selectors
Proceedings of the 13th Conference on Theoretical Aspects of Rationality and Knowledge
Tight bounds for strategyproof classification
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Algorithms for strategyproof classification
Artificial Intelligence
Mechanism design on discrete lines and cycles
Proceedings of the 13th ACM Conference on Electronic Commerce
Strategyproof facility location and the least squares objective
Proceedings of the fourteenth ACM conference on Electronic commerce
Approximate Mechanism Design without Money
ACM Transactions on Economics and Computation
Hi-index | 0.00 |
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.