Good learners for evil teachers
- aMicrosoft Research, Redmond, WA
- bThe Hebrew University, Jerusalem, Israel
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Abstract
We consider a supervised machine learning sce-nario where labels are provided by a hetero-geneous set of teachers, some of which are mediocre, incompetent, or perhaps even mali-cious. We present an algorithm, built on the SVM framework, that explicitly attempts to cope with low-quality and malicious teachers by decreas-ing their influence on the learning process. Our algorithm does not receive any prior information on the teachers, nor does it resort to repeated la-beling (where each example is labeled by mul-tiple teachers). We provide a theoretical analy-sis of our algorithm and demonstrate its merits empirically. Finally, we present a second algo-rithm with promising empirical results but with-out a formal analysis. 1.
TL;DRThis work presents an algorithm, built on the SVM framework, that explicitly attempts to cope with low-quality and malicious teachers by decreasing their influence on the learning process. (Semantic Scholar)