In this paper, we present a coded computation (CC) scheme for distribute...
Compute-forward multiple access (CFMA) is a multiple access transmission...
Neural Networks (GNNs) have recently emerged as a promising approach to
...
This paper examines the maximum code rate achievable by a data-driven
co...
We consider the problem of adaptive stabilization for discrete-time,
mul...
Due to mutual interference between users, power allocation problems in
w...
A recent line of works, initiated by Russo and Xu, has shown that the
ge...
We study the effectiveness of stochastic side information in determinist...
A private information retrieval (PIR) scheme allows a client to retrieve...
We propose a certainty-equivalence scheme for adaptive control of scalar...
Transfer learning, or domain adaptation, is concerned with machine learn...
Lagrange coded computation (LCC) is essential to solving problems about
...
The polar receiver architecture is a receiver design that captures the
e...
We introduce a novel tree coloring problem in which each node of a roote...
The establishment of the link between causality and unsupervised domain
...
A recent line of works, initiated by Russo and Xu, has shown that the
ge...
The use of 1-bit analog-to-digital converters (ADCs) is seen as a promis...
Transfer learning is a machine learning paradigm where knowledge from on...
Several information-theoretic studies on channels with output quantizati...
Transfer learning is a machine learning paradigm where the knowledge fro...
This paper establishes the capacity of additive white Gaussian noise (AW...
Semi-supervised learning algorithms attempt to take advantage of relativ...
Transfer learning, or domain adaptation, is concerned with machine learn...
In the Bitcoin white paper[1], Nakamoto proposed a very simple Byzantine...
Real-time data-driven optimization and control problems over networks ma...
We present a practical strategy that aims to attain rate points on the
d...
Building on the previous work of Lee et al. and Ferdinand et al. on code...