Active Learning with Disagreement Graphs
Corinna Cortes · Giulia DeSalvo · Mehryar Mohri · Ningshan Zhang · Claudio Gentile

Wed Jun 12th 04:40 -- 05:00 PM @ Room 201

We present two novel enhancements of an online importance-weighted active learning algorithm IWAL, using the properties of disagreements among hypotheses. The first enhancement, IWALD, prunes the hypothesis set with a more aggressive strategy based on the disagreement graph. We show that IWAL-D improves the generalization performance and the label complexity of the original IWAL, and quantify the improvement in terms of the disagreement graph coefficient. The second enhancement, IZOOM, further improves IWAL-D by adaptively zooming into the current version space and thus reducing the best-in-class error. We show that IZOOM admits favorable theoretical guarantees with the changing hypothesis set. We report experimental results on multiple datasets and demonstrate that the proposed algorithms achieve better test performances than IWAL given the same amount of labeling budget.

Author Information

Corinna Cortes (Google Research)
Giulia DeSalvo (Google Research)
Mehryar Mohri (Courant Institute and Google Research)
Ningshan Zhang (New York University)
Claudio Gentile (INRIA and Google)

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