Systems Seminar
Activized Learning: Transforming Passive to Active with Improved Label Complexity
Steve Hanneke
Machine Learning Department
School of Computer Science
Carnegie Mellon University
Abstract
In active learning, a learning algorithm is given access to a large
pool of unlabeled examples, and is allowed to request the labels of
any particular examples in that pool, interactively. In empirically
driven research, one of the most common techniques for designing new
active learning algorithms is to use an existing passive learning
algorithm as a subroutine, and actively construct a training set for
that method by carefully choosing informative examples to label. The
resulting active learning algorithms are thus able to inherit the
tried-and-true learning bias of the underlying passive algorithm,
while often requiring significantly fewer labels to achieve a given
accuracy compared to random sampling.
This naturally raises the theoretical question of whether every
passive learning algorithm can be "activized", or transformed into an
active learning algorithm that uses a smaller number of labels to
achieve a given accuracy. In this talk, I will address precisely this
question. In particular, I will explain how to use any passive
learning algorithm as a subroutine to construct an active learning
algorithm that provably achieves a strictly superior asymptotic label
complexity. Along the way, I will also describe some of the recently
developed mathematical tools for the formal study of active learning
in general.
Time and Place: Fri., Nov. 14, at 2:30 in 4610 Engr. Hall.
*** NOTE SPECIAL DAY and TIME ***
SYSTEMS SEMINAR WEB PAGE:
http://homepages.cae.wisc.edu/~gubner/seminar/schedule.html