Multiple-instance learning (MIL) is a generalization of the supervised learning problem where each training observation is a labeled bag of unlabeled instances. Several supervised ...
Methods for directly estimating the ratio of two probability density functions without going through density estimation have been actively explored recently since they can be used...
Policy gradient (PG) reinforcement learning algorithms have strong (local) convergence guarantees, but their learning performance is typically limited by a large variance in the e...
We present a polynomial-time approximation algorithm for legally coloring as many edges of a given simple graph as possible using two colors. It achieves an approximation ratio of ...
We present and evaluate the idea of adaptive processor cache management. Specifically, we describe a novel and general scheme by which we can combine any two cache management alg...
Ranjith Subramanian, Yannis Smaragdakis, Gabriel H...