Systems Seminar
Active Learning
Prof. Rob Nowak
UW ECE Department
Abstract
Traditional sampling and statistical learning theories deal with data
collection processes that are completely independent of the target
function to be estimated, aside from possible a priori specifications
reflective of assumed properties of the target. We refer to such
processes as passive learning methods. Alternatively, one can
envision adaptive sequential data collection procedures that use
information gleaned from previous observations to guide the process.
We refer to such feedback-driven processes as active learning methods.
While there have been many successful practical applications of active
learning methods, there is scant theoretical evidence to support the
effectiveness of active over passive learning. This talk covers some
of the most encouraging theoretical results to date, and focuses on
new results regarding the capabilities of active methods for learning
(nonparametric) smooth and piecewise smooth function spaces.
Significantly faster rates of error convergence are achieved by active
learning compared to passive learning in cases involving functions
whose complexity (in the Kolmogorov sense) is highly concentrated
within small regions its domain (e.g., functions that are smoothly
varying apart from highly localized abrupt changes such as jumps or
edges).
This is joint work with Rui Castro and Rebecca Willett. Please see
our on-line technical report for further details:
http://www.ece.wisc.edu/~nowak/ECE-05-03.pdf
Time and Place: Wed., Sept. 28, at 3:30 pm in 4610 Engr. Hall.
SYSTEMS SEMINAR WEB PAGE:
http://homepages.cae.wisc.edu/~gubner/seminar/