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- W2950119841 abstract "We consider the problem of binary prediction with expert advice in settings where experts have agency and seek to maximize their credibility. This paper makes three main contributions. First, it defines a model to reason formally about settings with selfish experts, and demonstrates that incentive compatible (IC) algorithms are closely related to the design of proper scoring rules. Designing a good IC algorithm is easy if the designer's loss function is quadratic, but for other loss functions, novel techniques are required. Second, we design IC algorithms with good performance guarantees for the absolute loss function. Third, we give a formal separation between the power of online prediction with selfish experts and online prediction with honest experts by proving lower bounds for both IC and non-IC algorithms. In particular, with selfish experts and the absolute loss function, there is no (randomized) algorithm for online prediction-IC or otherwise-with asymptotically vanishing regret." @default.
- W2950119841 created "2019-06-27" @default.
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- W2950119841 creator A5046785442 @default.
- W2950119841 date "2017-02-13" @default.
- W2950119841 modified "2023-09-27" @default.
- W2950119841 title "Online Prediction with Selfish Experts" @default.
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- W2950119841 doi "https://doi.org/10.5555/3294771.3294895" @default.
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