We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and wh...
We consider a classic multidimensional generalization of the bin packing problem, namely, packing d-dimensional rectangles into the minimum number of unit cubes. Our two results a...
—Thus far, sparse representations have been exploited largely in the context of robustly estimating functions in a noisy environment from a few measurements. In this context, the...
Abstract. We propose a distributed scheme called Adaptive-GroupMerge for sensor networks that, given a parameter k, approximates a geometric shape by a k-vertex polygon. The algori...
Chiranjeeb Buragohain, Sorabh Gandhi, John Hershbe...
In this paper, we investigate the use of parallelization in reinforcement learning (RL), with the goal of learning optimal policies for single-agent RL problems more quickly by us...