The central issue in representing graphstructured data instances in learning algorithms is designing features which are invariant to permuting the numbering of the vertices. We pr...
We design and analyze interacting online algorithms for multitask classification that perform better than independent learners whenever the tasks are related in a certain sense. W...
In this paper we consider approximate policy-iteration-based reinforcement learning algorithms. In order to implement a flexible function approximation scheme we propose the use o...
Amir Massoud Farahmand, Mohammad Ghavamzadeh, Csab...
Training principles for unsupervised learning are often derived from motivations that appear to be independent of supervised learning. In this paper we present a simple unificatio...
The idea of local learning, i.e., classifying a particular example based on its neighbors, has been successfully applied to many semi-supervised and clustering problems recently. ...