We consider the problem of choosing, sequentially, a map which assigns elements of a set A to a few elements of a set B. On each round, the algorithm suffers some cost associated ...
Alexander Rakhlin, Jacob Abernethy, Peter L. Bartl...
The choice of the kernel function which determines the mapping between the input space and the feature space is of crucial importance to kernel methods. The past few years have se...
We present a new, statistical approach to rule learning. Doing so, we address two of the problems inherent in traditional rule learning: The computational hardness of finding rule...
This paper considers the problem of selecting the most informative experiments x to get measurements y for learning a regression model y = f(x). We propose a novel and simple conc...
The problem of learning with positive and unlabeled examples arises frequently in retrieval applications. We transform the problem into a problem of learning with noise by labelin...