Good Learners for Evil Teachers
Ofer Dekel and Ohad Shamir
In: ICML 2009(2009).
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.
|EPrint Type:||Conference or Workshop Item (Paper)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||Ohad Shamir|
|Deposited On:||24 June 2009|