We obtain a tight distribution-specific characterization of the sample complexity of large-margin classification with L2 regularization: We introduce the -adapted-dimension, which...
—This paper considers feature selection for data classification in the presence of a huge number of irrelevant features. We propose a new feature selection algorithm that addres...
Learning is a task that generalizes many of the analyses that are applied to collections of data, and in particular, collections of sensitive individual information. Hence, it is n...
We propose a formulation of the Decision Tree learning algorithm in the Compression settings and derive tight generalization error bounds. In particular, we propose Sample Compres...
We develop a theory for learning scenarios where multiple learners co-exist but there are mutual compatibility constraints on their outcomes. This is natural in cognitive learning...