Good learners for evil teachers

We consider a supervised machine learning scenario where labels are provided by a heterogeneous set of teachers, some of which are mediocre, incompetent, or perhaps even malicious. We present 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. Our algorithm does not receive any prior information on the teachers, nor does it resort to repeated labeling (where each example is labeled by multiple teachers). We provide a theoretical analysis of our algorithm and demonstrate its merits empirically. Finally, we present a second algorithm with promising empirical results but without a formal analysis.

• Type: Conference
• Year: 2009
• Where: ICML
• Authors: Ofer Dekel, Ohad Shamir
• Keywords: ICML 2009, Machine Learning, Machine Learning Scenario, Malicious Teachers, Multiple Teachers