Systems | Information | Learning | Optimization
 

Active Ranking using Pairwise Comparisons | Decomposition Methods for Large Scale LP Decoding

Title: Active Ranking using Pairwise Comparisons by Kevin Jamieson This talk examines the problem of ranking a collection of objects using pairwise comparisons (rankings of two objects). In general, the ranking of n objects can be identified by standard sorting methods using n log2 n pairwise comparisons. We are interested …

Metamaterial Lens Modeling for mm-wave MIMO Communications | Robust Data Analysis Algorithms in a Neutrino Telescope

Title: Metamaterial Lens Modeling for mm-wave MIMO Communications by John Brady The capacity requirements of wireless communications are expanding rapidly with the proliferation of consumer wireless devices. Millimeter wave communication systems are uniquely suited to high data-rate applications not only due to large bandwidths but also small wavelengths that enable …

Cooperative Communication: Backpressure Algorithm with Mutual Information Accumulation | Online robust PCA and examples in computer vision

Cooperative Communication: Backpressure Algorithm with Mutual Information Accumulation By Nick Yanpei Liu We develop scheduling policies that maximize the stability region of a wireless network under the assumption that mutual information accumulation is implemented at the physical layer. When the link quality between nodes is not sufficiently high that a …

Music Hack

Andrew Bridy and Lalit Jain will tell us about their experience at Boston’s Music Hack Day. They’ll describe their metamorphosis from number theorists to music hackers, their awesome new hack, and their perspectives on the future of music.

Theory and software for sparse approximation algorithm phase transitions.

Abstract: We review the eigen-analysis and convex polytope approaches for the development of sparse approximation and compressed sensing algorithms. Problems which can be recast as convex relaxations are amenable to a precise analysis, but non-convex formulated algorithms have a dramatically less precise theoretical understanding. We present a gpu accelerated software …

Restricted Isometry Constants in Compressed Sensing | Network localization with some new wrinkles: Noncovex geometry in low dimension

Bubacarr’s talk: ABSTRACT: Restricted Isometry Constants (RICs) of a matrix are a popular tool in the analysis of compressed sensing algorithms. The best known bounds will be presented for Gaussian matrices as well as expander graphs. In the former case we will also present explicit formulae for the bounds in …

An optimal architecture for poset-causal systems | Computable Bounds on Sparsity Recovery

Gongguo’s talk: Title: Computable Bounds on Sparsity Recovery Abstract: The performance of sparsity recovery depends on the structures of the sensing matrices. The quality of these matrices in the context of signal recovery is usually quantified by the restricted isometry constant and its variants. However, the restricted isometry constant and …