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CMU-ML-12-112
Machine Learning Department
School of Computer Science, Carnegie Mellon University
CMU-ML-12-112
Bounds on the Minimax Rate for Estimating a
Prior over a VC Class from Independent Learning Tasks
Liu Yang, Steve Hanneke, Jaime Carbonell
December 2012
CMU-ML-12-112.pdf
Keywords:
Minimax Rates, Transfer Learning, VC Dimension, Bayesian Learning
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.
12 pages
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