University of Cambridge > Talks.cam > Statistics > Efficient and Parsimonious Agnostic Active Learning

  Browse A-Z

Log in

Cambridge users (raven)details Other usersdetails No account?details

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

Efficient and Parsimonious Agnostic Active Learning

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Quentin Berthet.

We develop a new active learning algorithm for the streaming setting satisfying three important properties: 1) It provably works for any classifier representation and classification problem including those with severe noise. 2) It is efficiently implementable with an ERM oracle. 3) It is more aggressive than all previous approaches satisfying 1 and 2. To do this we create an algorithm based on a newly defined optimization problem and analyze it. We also conduct the first experimental analysis of all efficient agnostic active learning algorithms, evaluating their strengths and weaknesses in different settings.

This is joint work with Tzu-Kuo Huang, John Langford and Rob Schapire at Microsoft Research.

This talk is part of the Statistics series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

The obesity epidemic: Discussing the global health crisis Islamic Society Considering Performance: A Symposium of American Culture and Literature

Other talks

Challenges to monetary policy in a global context Regulators of Muscle Stem Cell Fate and Function New micro-machines, new materials Chemical convection and stratification at the top of the Earth's outer core Beyond truth-as-correspondence: realism for realistic people NatHistFest: the 99th Conversazione and exhibition on the wonders of the natural world.
 

© 2006-2021 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity