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Creators/Authors contains: "Ananthakrishnan, Nivasini"

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  1. ; ; ; (, Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research)
    Motivated by the emergence of decentralized machine learning (ML) ecosystems, we study the delegation of data collection. Taking the field of contract theory as our starting point, we design optimal and near-optimal contracts that deal with two fundamental information asymmetries that arise in decentralized ML: uncertainty in the assessment of model quality and uncertainty regarding the optimal performance of any model. We show that a principal can cope with such asymmetry via simple linear contracts that achieve $$1-1/\epsilon$$ fraction of the optimal utility. To address the lack of a priori knowledge regarding the optimal performance, we give a convex program that can adaptively and efficiently compute the optimal contract. We also analyze the optimal utility and linear contracts for the more complex setting of multiple interactions. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; (, Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research)
    Motivated by the emergence of decentralized machine learning (ML) ecosystems, we study the delegation of data collection. Taking the field of contract theory as our starting point, we design optimal and near-optimal contracts that deal with two fundamental information asymmetries that arise in decentralized ML: uncertainty in the assessment of model quality and uncertainty regarding the optimal performance of any model. We show that a principal can cope with such asymmetry via simple linear contracts that achieve $$1-1/\epsilon$$ fraction of the optimal utility. To address the lack of a priori knowledge regarding the optimal performance, we give a convex program that can adaptively and efficiently compute the optimal contract. We also analyze the optimal utility and linear contracts for the more complex setting of multiple interactions. 
    more » « less
    Accepted Manuscript Full Text Available