In many complex machine learning applications there is a need to learn multiple interdependent output variables, where knowledge of these interdependencies can be exploited to impr...
k. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multi...
Multi-label learning deals with ambiguous examples each may belong to several concept classes simultaneously. In this learning framework, the inherent ambiguity of each example is...
We present a reinforcement learning game player that can interact with a General Game Playing system and transfer knowledge learned in one game to expedite learning in many other ...
Metric learning algorithms can provide useful distance functions for a variety of domains, and recent work has shown good accuracy for problems where the learner can access all di...
Prateek Jain, Brian Kulis, Inderjit S. Dhillon, Kr...