Learning how to refer effectively in an expert-referral network is an emerging challenge at the intersection of Active Learning and MultiAgent Reinforcement Learning. Distributed interval estimation learning (DIEL) was previously found to be promising for learning appropriate referral choices, compared to greedy and Q-learning methods. This paper extends these results in several directions: Fir...

Note that vertex cover is a special case of set cover where U is the set of all edges and each vertex v is a subset in S which contains all edges incident to v. In this case, each element is in exactly two subsets in S. We present a greedy approximation algorithm for Set Cover. This is probably the most natural greedy algorithm for this problem. The idea is, at each iteration pick a set where t...

This article presents the development and application of a model that can be utilized to compare autonomy principals in various historical national contexts. Drawing on former conceptual work education, conceptualizes principal as two-dimensional. The first dimension is decision making expected from principals, manifested continuum being restricted or extended. second control imposed also varyi...

In this paper, we investigate how to design energy-efficient localized routing in a large-scale three-dimensional (3D) wireless network. Several 3D localized routing protocols were proposed to seek either energy efficiency or delivery guarantee in 3D wireless networks. However, recent results [1, 2] showed that there is no deterministic localized routing algorithm that guarantees either deliver...

We show how to use unlabeled data and a deep belief net (DBN) to learn a good covariance kernel for a Gaussian process. We first learn a deep generative model of the unlabeled data using the fast, greedy algorithm introduced by [7]. If the data is high-dimensional and highly-structured, a Gaussian kernel applied to the top layer of features in the DBN works much better than a similar kernel app...

In [5], a reduced basis method (RBM) for the electric field integral equation (EFIE) based on the boundary element method (BEM) is developed, based on a simplified a posteriori error estimator for the Greedy-based snapshot selection. In this paper, we extend this work and propose a certified RBM for the EFIE based on a mathematically rigorous a posteriori estimator. The main difficulty of the c...

An efficient way to learn deep density models that have many layers of latent variables is to learn one layer at a time using a model that has only one layer of latent variables. After learning each layer, samples from the posterior distributions for that layer are used as training data for learning the next layer. This approach is commonly used with Restricted Boltzmann Machines, which are und...