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• W2247708794 abstract "We study the optimal rates of convergence for estimating a prior distribution over a VC class from a sequence of independent data sets respectively labeled by independent target functions sampled from the prior. We specifically derive upper and lower bounds on the optimal rates under a smoothness condition on the correct prior, with the number of samples per data set equal the VC dimension. These results have implications for the improvements achievable via transfer learning. We additionally extend this setting to real-valued function, where we establish consistency of an estimator for the prior, and discuss an additional application to a preference elicitation problem in algorithmic economics." @default.
• W2247708794 created "2016-06-24" @default.
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• W2247708794 date "2015-01-01" @default.
• W2247708794 modified "2023-09-25" @default.
• W2247708794 title "Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks" @default.
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• W2247708794 doi "https://doi.org/10.1007/978-3-319-24486-0_18" @default.
• W2247708794 hasPublicationYear "2015" @default.
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