64 citations · 222 across the 17 of their papers we have counts for
10 papers · 1 filter
Information Bottleneck Methods for Distributed Learning
Parinaz Farajiparvar, Ahmad Beirami, Matthew Nokleby
We study a distributed learning problem in which Alice sends a compressed distillation of a set of training data to Bob, who uses the distilled version to best solve an associated…
Tensor Matched Kronecker-Structured Subspace Detection for Missing Information
Ishan Jindal, Matthew Nokleby
We consider the problem of detecting whether a tensor signal having many missing entities lies within a given low dimensional Kronecker-Structured (KS) subspace. This is a matched…
Multi-Scale Spectrum Sensing in Dense Multi-Cell Cognitive Networks
Nicolo Michelusi, Matthew Nokleby, Urbashi Mitra +1
Multi-scale spectrum sensing is proposed to overcome the cost of full network state information on the spectrum occupancy of primary users (PUs) in dense multi-cell cognitive netwo…
Classification and Representation via Separable Subspaces: Performance Limits and Algorithms
Ishan Jindal, Matthew Nokleby
We study the classification performance of Kronecker-structured models in two asymptotic regimes and developed an algorithm for separable, fast and compact K-S dictionary learning…
Multi-scale Spectrum Sensing in Small-Cell mm-Wave Cognitive Wireless Networks
Nicolo Michelusi, Matthew Nokleby, Urbashi Mitra +1
In this paper, a multi-scale approach to spectrum sensing in cognitive cellular networks is proposed. In order to overcome the huge cost incurred in the acquisition of full network…
Low-Dimensional Shaping for High-Dimensional Lattice Codes
Nuwan S. Ferdinand, Brian M. Kurkoski, Matthew Nokleby +1
We propose two low-complexity lattice code constructions that have competitive coding and shaping gains. The first construction, named systematic Voronoi shaping, maps short blocks…
Rate-Distortion Bounds on Bayes Risk in Supervised Learning
Matthew Nokleby, Ahmad Beirami, Robert Calderbank
We present an information-theoretic framework for bounding the number of labeled samples needed to train a classifier in a parametric Bayesian setting. We derive bounds on the aver…
Discrimination on the Grassmann Manifold: Fundamental Limits of Subspace Classifiers
Matthew Nokleby, Miguel Rodrigues, Robert Calderbank
We present fundamental limits on the reliable classification of linear and affine subspaces from noisy, linear features. Drawing an analogy between discrimination among subspaces a…
Toward Resource-Optimal Consensus over the Wireless Medium
Matthew Nokleby, Waheed U. Bajwa, Robert Calderbank +1
We carry out a comprehensive study of the resource cost of averaging consensus in wireless networks. Most previous approaches suppose a graphical network, which abstracts away cruc…
Cooperative Compute-and-Forward
Matthew Nokleby, Behnaam Aazhang
We examine the benefits of user cooperation under compute-and-forward. Much like in network coding, receivers in a compute-and-forward network recover finite-field linear combinati…