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The Prediction Advantage: A Universally Meaningful Performance Measure for Classification and Regression
We introduce the Prediction Advantage (PA), a novel performance measure for prediction functions under any loss function (e. g., classification or regression).
A Compression Technique for Analyzing Disagreement-Based Active Learning
We introduce a new and improved characterization of the label complexity of disagreement-based active learning, in which the leading quantity is the version space compression set size.
Pointwise Tracking the Optimal Regression Function
This paper examines the possibility of a `reject option' in the context of least squares regression.
Agnostic Selective Classification
For a learning problem whose associated excess loss class is $(\beta, B)$-Bernstein, we show that it is theoretically possible to track the same classification performance of the best (unknown) hypothesis in our class, provided that we are free to abstain from prediction in some region of our choice.
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