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Importance Weighted Active Learning

Importance Weighted Active Learning. by Alina Beygelzimer, Sanjoy Dasgupta and John Langford ( ICML 2009 ). Presented by Lingbo Li ECE, Duke University September 25, 2009. Outline. Introduction The Importance Weighting Skeleton Setting the rejection threshold Label Complexity

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Importance Weighted Active Learning

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  1. Importance Weighted Active Learning by Alina Beygelzimer, Sanjoy Dasgupta and John Langford (ICML 2009) Presented by Lingbo Li ECE, Duke University September 25, 2009

  2. Outline • Introduction • The Importance Weighting Skeleton • Setting the rejection threshold • Label Complexity • Implementing IWAL • Conclusion

  3. 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.

  4. The Importance Weighting Skeleton • The expected loss • The importance weighted estimate of the loss at time T then • IWAL algorithms are consistent, if does not equal zero.

  5. Setting the rejection threshold • To do the minimization over instead of where • IWAL performs never worse than supervised learning.

  6. Label Complexity – upper bound Previous work of active learning has been done only on the 0-1 loss with the number of queries of ; For arbitrary loss functions with the similar conditions, the number of queries is

  7. Label Complexity – lower bound Lower bound is increased.

  8. 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 (1)

  9. Implementing IWAL (2) bootstrapping scheme binary and multiclass classification loss MNIST dataset

  10. 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.

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