Making Your Head Explode and Other Classroom Delights: The Importance of Active Learning
Importance Weighted Active Learning
description
Transcript of Importance Weighted Active Learning
Importance Weighted Active Learning
by
Alina Beygelzimer, Sanjoy Dasgupta and John Langford
(ICML 2009)
Presented by Lingbo LiECE, Duke UniversitySeptember 25, 2009
Outline
• Introduction
• The Importance Weighting Skeleton
• Setting the rejection threshold
• Label Complexity
• Implementing IWAL
• Conclusion
Introduction
• Active learning At each step t, a learner receives an unlabeled point , and decide whether to query its
label . Hypothesis space is , where Z is prediction space.
Loss function
• Drawback from earlier work: not consistent• PAC-convergence guarantee active learning
1) only 0-1 loss function; 2) internal use of generalization bounds.
• Importance weighted approach
1) non-adaptive; 2) asymptotic.
• Motivation Using importance weighting to build a consistent binary classifier under general
loss functions, which removes sampling bias and improves label complexity.
tx Xty Y :{ : }H h X Z
: [0, )l Z Y
The Importance Weighting Skeleton
X Y
• The expected loss
• The importance weighted estimate of the loss at time T
then
• IWAL algorithms are consistent, if
does not equal zero.
tp
Setting the rejection threshold
H
• To do the minimization over instead of
where
• IWAL performs never worse than supervised learning.
tHH
Label Complexity – upper bound
2( log )O T dT T 2( log )O T d T
2( log )O T dT T
Previous work of active learning has been done only on the 0-1 loss with the number of queriesof ; For arbitrary loss functions with the similar conditions, the number of queries is
Label Complexity – lower bound
Lower bound is increased.
Implementing IWAL (1)
• linear separators;
• logistic loss;
• MNIST data set of handwritten digits with 3’s and 5’s as two classes;
• 1000 exemplars for training;
• another 1000 for testing;
• Use PCA to reduce dimensions;
• Optimistic bound of
• Active learning performs similar to supervised learning with only less than 1/3 of the labels queried.
Implementing IWAL (2)bootstrapping schemebinary and multiclass classification loss MNIST dataset
tp
Conclusion
• IWAL is a consistent algorithm, which can be implemented with flexible losses.
• Label complexity is theoretical provided with substantial improvement.
• Practical experiments approve this.