Incentive compatible regression learning
From MaRDI portal
Publication:1959425
DOI10.1016/j.jcss.2010.03.003zbMath1208.68132OpenAlexW1983916796MaRDI QIDQ1959425
Ofer Dekel, Ariel D. Procaccia, Felix Fischer
Publication date: 7 October 2010
Published in: Journal of Computer and System Sciences (Search for Journal in Brave)
Full work available at URL: https://drops.dagstuhl.de/opus/volltexte/2007/1162/
Computational learning theory (68Q32) Learning and adaptive systems in artificial intelligence (68T05) Rationality and learning in game theory (91A26)
Related Items (7)
Optimal Impartial Selection ⋮ A Survey on Approximation Mechanism Design Without Money for Facility Games ⋮ GOODHART'S LAW AND MACHINE LEARNING: A STRUCTURAL PERSPECTIVE ⋮ Algorithms for strategyproof classification ⋮ On incentive-compatible estimators ⋮ Regression markets and application to energy forecasting ⋮ Optimally Deceiving a Learning Leader in Stackelberg Games
Cites Work
- Unnamed Item
- Unnamed Item
- The learnability of voting rules
- Reducing mechanism design to algorithm design via machine learning
- Decision theoretic generalizations of the PAC model for neural net and other learning applications
- Strategy-proofness and Arrow's conditions: existence and correspondence theorems for voting procedures and social welfare functions
- Learnability and rationality of choice.
- Strategy-proof estimators for simple regression.
- PAC learning with nasty noise.
- Can PAC learning algorithms tolerate random attribute noise?
- Generalized Condorcet-winners for single peaked and single-plateau preferences
- Inventory Management of a Fast-Fashion Retail Network
- Learning in the Presence of Malicious Errors
- Thirteen Reasons Why the Vickrey-Clarke-Groves Process Is Not Practical
- Truth revelation in approximately efficient combinatorial auctions
- Incentives in Teams
- Manipulation of Voting Schemes: A General Result
- Straightforward Elections, Unanimity and Phantom Voters
- 10.1162/153244303321897690
- Truthful randomized mechanisms for combinatorial auctions
- Convergence of stochastic processes
- Algorithmic mechanism design
This page was built for publication: Incentive compatible regression learning