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Incentive Compatible Regression Learning Dekel, Ofer Fischer, Felix Procaccia, Ariel D. Machine learning regression mechanism design 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, which are interested in different distributions over the sample space. This conflict potentially gives rise to untruthfulness on the part of the agents. In the restricted but important case when distributions are degenerate, 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 efficiency. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Ofer Dekel and Felix Fischer and Ariel D. Procaccia 2007 Is Part Of Dagstuhl Seminar Proceedings, Volume 7271, Computational Social Systems and the Internet (2007) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagSemProc.07271.6 urn:nbn:de:0030-drops-11622 https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07271.6 eng https://creativecommons.org/licenses/by/4.0/legalcode