The question of how and whether to join physical layer and network coding remains actively investigated. In this talk, we present some recent information-theoretic results and an application illustrating a view of this question. We begin by considering whether there are benefits, in capacity, from integrating network and physical layer …
We will discuss two mechanisms for dealing with integer programs that contain a great deal of symmetry—orbital branching and isomorphism pruning. These methods use information encoded in the symmetry group of the integer program to guide the branching decision and prune nodes of the search tree. Orbital branching and isomorphism …
We describe a novel approach to online convex programming in dynamic settings. Many existing online learning methods are characterized via regret bounds that quantify the gap between the performance of the online algorithm relative to a comparator. In previous work, this comparator was either considered static over time, or admitted …
This presentation will review the state of the art in atom probe tomography (APT) and emphasize the successes and remaining challenges. The presentation will conclude with a look toward future developments that will enhance APT. One of the key challenges is information processing such as reconstruction of our images and …
We present a simple technique for estimating parts of the Singular Value Decomposition (SVD) of a data matrix from a small randomly compressed “sketch” of that matrix. In sensor network settings–where each column of the data matrix comes from a separate sensor–the sketch can be assembled using operations local to …
Large-scale computational simulation using numerical methods is now widespread in science and engineering. Good approximation methods exist for many problems and fast linear solvers for the resulting linear(ized) equations too. In this talk we will focus on finite element approximations and iterative linear equation solvers. We intend to show how …
An extremely powerful tool in the field of topological data analysis (TDA) is persistent homology which seeks to quantify structure in a large data set. In particular, persistence can find homology classes for an underlying manifold even when obscured by noise. These homology classes can then give information about the …
Many scientific and engineering applications rely upon the accurate reconstruction of spatially, spectrally, and temporally distributed phenomena from photon-limited data. When the number of observed events is very small, accurately extracting knowledge from this data requires the development of both new computational methods and novel theoretical analysis frameworks. This task …
In various decision making problems, one is given the advice or predictions of several classifiers of unknown reliability, over multiple questions or queries. This scenario is different from the standard supervised setting where classifier accuracy can be assessed using available labeled training or validation data, and raises several questions: given …
We consider the problem of learning a directed acyclic graph (DAG) model based on conditional independence testing. The most widely used approaches to this problem are variants of the PC algorithm. One of the drawbacks of the PC algorithm is that it requires the strong-faithfulness assumption, which has been show …