Machine Learning Department
School of Computer Science, Carnegie Mellon University


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


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

SCS Technical Report Collection
School of Computer Science