Machine Learning Department
School of Computer Science, Carnegie Mellon University
Bounds on the Minimax Rate for Estimating a
Liu Yang, Steve Hanneke, Jaime Carbonell
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.
||SCS Technical Report Collection
School of Computer Science