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Efficient and Parsimonious Agnostic Active Learning
Tzu-Kuo Huang · Alekh Agarwal · Daniel Hsu · John Langford · Robert Schapire
We develop a new active learning algorithm for the streaming settingsatisfying three important properties: 1) It provably works for anyclassifier representation and classification problem including thosewith severe noise. 2) It is efficiently implementable with an ERMoracle. 3) It is more aggressive than all previous approachessatisfying 1 and 2. To do this, we create an algorithm based on a newlydefined optimization problem and analyze it. We also conduct the firstexperimental analysis of all efficient agnostic active learningalgorithms, evaluating their strengths and weaknesses in differentsettings.
Author Information
T.-K. Huang (Microsoft)
Alekh Agarwal (Microsoft Research)
Daniel Hsu (Columbia University)
John Langford (Microsoft Research New York)
Robert Schapire (MIcrosoft Research)
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