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\title{Robust Learning with FeatureBoost}

\author{Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum \\
School of Computer Science\\ 
Carnegie-Mellon University\\
Pittsburgh, PA 15213 \\
{\it \{josullvn,jcl,caruana,blum\}@cs.cmu.edu} \\
}

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\begin{abstract}
PAC learning typically assumes that the training and test sets are
drawn from the same distributions.  This assumption often is violated
in practice.  How can machine learning algorithms be biased to output
hypotheses that are robust to alterations in the test distribution?
We create a framework for learning in environments where the test and
training distributions differ because features in the test sets are
likely to be missing or corrupted.  Motivated by this framework, we
present a new meta-learning algorithm, FeatureBoost.  We demonstrate
FeatureBoost on three learning problems using backprop nets, k-nearest
neighbor, and decision trees.
\end{abstract}



\vspace*{1.0in}
Corresponding Author: Rich Caruana

Category: Algorithms \& Architecture

Oral Presentation Preferred

This work has not been submitted or published elsewhere.
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