We consider the problem of multiple kernel learning (MKL), which can be formulated as a convex-concave problem. In the past, two efficient methods, i.e., Semi-Infinite Linear Prog...
Current research in autonomic computing suffers from the lack of a common definition of the basic autonomic entities. Defining and developing the basic autonomic entities and maki...
M. Muztaba Fuad, Debzani Deb, Michael J. Oudshoorn
Markov Decision Processes (MDPs) have been extensively studied and used in the context of planning and decision-making, and many methods exist to find the optimal policy for probl...
Linear Programming (LP) relaxations have become powerful tools for finding the most probable (MAP) configuration in graphical models. These relaxations can be solved efficiently u...
David Sontag, Talya Meltzer, Amir Globerson, Tommi...
In many real world planning scenarios, agents often do not have enough resources to achieve all of their goals. Consequently, they are forced to find plans that satisfy only a sub...
Menkes van den Briel, Romeo Sanchez Nigenda, Minh ...