Efficient Algorithms for Active Learning
Department of Computer Science and Engineering
University of California, San Diego
Wednesday, April 2nd, 2008
Troy 2018 - 11:00 a.m. to 12:00 p.m.
Refreshments at 10:30 a.m.
The rapidly increasing abundance of data generated by internet
transactions, satellite measurements, and environmental sensors, among
other sources, creates new and urgent challenges for machine learning.
My work on machine learning theory and algorithms is motivated by the
problems posed by real-world data. This talk will focus on providing
efficient algorithms for active learning, a rich model for learning when
labels are missing.
In many data-rich problems, unlabeled data is much easier to obtain than
labeled data (e.g. images on the web, unlabeled speech or video signal).
An active learner receives unlabeled data and can make intelligent
choices about which labels to request via some mechanism, for example by
paying for annotation by a human. Active learning is useful in many
practical applications, such as spam filtering, however most previous
algorithms either lack formal performance guarantees, or are
computationally prohibitive. First I will present an algorithm that,
for uniformly distributed, linearly separable data, needs exponentially
fewer labels than the analogous sample complexity of supervised learning.
Our algorithm is extremely light-weight and easy to implement, and
respects online constraints on time and memory. Then I will discuss our
recent work on generalizing active learning to arbitrary data
distributions and hypothesis classes, with no separability assumption.
We provide an algorithm for general active learning via reduction, which
is as efficient (up to polylogarithmic factors) as the supervised
learning algorithm for the problem.
I will also mention other projects, including learning with online
constraints, privacy-preserving machine learning, applications to
networks and vision, and climate informatics.
Claire Monteleoni is currently a postdoc in Computer Science and
Engineering, at the University of California, San Diego. Her research
focuses on machine learning theory and algorithms, motivated by
practical applications. She completed her Ph.D. in 2006, and her
Masters in 2003, in Computer Science, at MIT. She did her undergraduate
work at Harvard, and grew up in New York City.
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For more information:
See Doctor Monteleoni's web page
Last updated: September 7, 2005