This paper considers the design of optimal resource allocation policies in wireless
communication systems, which are generically modeled as a functional optimization …

Learned optimizers---neural networks that are trained to act as optimizers---have the
potential to dramatically accelerate training of machine learning models. However, even …

Advanced technologies are required in future mobile wireless networks to support services
with highly diverse requirements in terms of high data rate and reliability, low latency, and …

With the superior capability of discovering intricate structure of large data sets, deep learning
has been widely applied in various areas including wireless networking. While existing deep …

Signal processing, communications, and control have traditionally relied on classical
statistical modeling techniques. Such model-based methods tend to be sensitive to …

While the fifth-generation systems are being rolled out across the globe, researchers have
turned their attention to the exploration of radical next-generation solutions. At this early …

Future wireless communication networks tend to be intelligentized to accomplish the
missions that cannot be preprogrammed. In the new intelligent communication systems …

Neural networks dominate the modern machine learning landscape, but their training and
success still suffer from sensitivity to empirical choices of hyperparameters such as model …

This short monograph contains the material expanded from two tutorials that the authors
gave, one at APSIPA in October 2011 and the other at ICASSP in March 2012. Substantial …

The fields of machine learning and mathematical programming are increasingly intertwined.
Optimization problems lie at the heart of most machine learning approaches. The Special …