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Spotlight
Robust Classification Under Sample Selection Bias
Anqi Liu · Brian Ziebart

Wed Dec 10 02:40 PM -- 03:05 PM (PST) @ Level 2, room 210
in Spotlight Session 8: GP spotlights, kernel spotlights, sampling spotlights, classification spotlights »

In many important machine learning applications, the source distribution used to estimate a probabilistic classifier differs from the target distribution on which the classifier will be used to make predictions. Due to its asymptotic properties, sample-reweighted loss minimization is a commonly employed technique to deal with this difference. However, given finite amounts of labeled source data, this technique suffers from significant estimation errors in settings with large sample selection bias. We develop a framework for robustly learning a probabilistic classifier that adapts to different sample selection biases using a minimax estimation formulation. Our approach requires only accurate estimates of statistics under the source distribution and is otherwise as robust as possible to unknown properties of the conditional label distribution, except when explicit generalization assumptions are incorporated. We demonstrate the behavior and effectiveness of our approach on synthetic and UCI binary classification tasks.

Author Information

Anqi Liu (Caltech)
Brian Ziebart (University of Illinois at Chicago)

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