Generalized linear models (GLMs) are used in high-dimensional machine learning,
statistics, communications, and signal processing. In this paper we analyze GLMs when the …

Is compressive sensing overrated? Or can it live up to our expectations? What will come
after compressive sensing and sparsity? And what has Galileo Galilei got to do with it …

In this theory paper, we investigate training deep neural networks (DNNs) for classification
via minimizing the information bottleneck (IB) functional. We show that the resulting …

We consider a class of approximated message passing (AMP) algorithms and characterize
their high-dimensional behavior in terms of a suitable state evolution recursion. Our proof …

This paper presents a framework for compressed sensing that bridges a gap between
existing theory and the current use of compressed sensing in many real-world applications …

Compressed sensing is a signal processing method that acquires data directly in a
compressed form. This allows one to make fewer measurements than were considered …

The concept of entropy has played a significant role in thermodynamics and information
theory, and is also a current research hotspot. Information entropy, as a measure of …

Signal recovery from unitarily invariant measurements is investigated in this paper. A
message-passing algorithm is formulated on the basis of expectation propagation (EP). A …

We study the compressed sensing reconstruction problem for a broad class of random, band-
diagonal sensing matrices. This construction is inspired by the idea of spatial coupling in …

We investigate the convergence rate of the optimal entropic cost v ε to the optimal transport
cost as the noise parameter ε↓ 0. We show that for a large class of cost functions c on R d× …