We derive information-theoretic lower bounds on the Bayes risk and
gener...
Distributed optimization is vital in solving large-scale machine learnin...
Emerging applications of machine learning in numerous areas involve
cont...
We propose a graph neural network (GNN) approach to the problem of predi...
Because large, human-annotated datasets suffer from labeling errors, it ...
In this paper, we develop a reinforcement learning (RL) based system to ...
We study a distributed learning problem in which Alice sends a compresse...
We consider the problem of detecting whether a tensor signal having many...
A multi-scale approach to spectrum sensing is proposed to overcome the h...
In building intelligent transportation systems such as taxi or rideshare...
Large datasets often have unreliable labels-such as those obtained from
...
Kronecker-structured (K-S) models recently have been proposed for the
ef...
Motivated by machine learning applications in networks of sensors,
inter...
We present an information-theoretic framework for bounding the number of...