Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many
signal processing and machine learning (ML) applications. It is used for solving optimization …

Modern signal processing (SP) methods rely very heavily on probability and statistics to
solve challenging SP problems. SP methods are now expected to deal with ever more …

Structured weight pruning is a representative model compression technique of DNNs to
reduce the storage and computation requirements and accelerate inference. An automatic …

Model compression is an important technique to facilitate efficient embedded and hardware
implementations of deep neural networks (DNNs), a number of prior works are dedicated to …

Distributed optimization algorithms are highly attractive for solving big data problems. In
particular, many machine learning problems can be formulated as the global consensus …

When generating adversarial examples to attack deep neural networks (DNNs), Lp norm of
the added perturbation is usually used to measure the similarity between original image and …

We consider a generic convex optimization problem associated with regularized empirical
risk minimization of linear predictors. The problem structure allows us to reformulate it as a …

Abstract The Alternating Direction Method of Multipliers (ADMM) has received lots of
attention recently due to the tremendous demand from large-scale and data-distributed …

Large deep neural network (DNN) models pose the key challenge to energy efficiency due
to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or …

Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse
problems by exploiting statistical priors specified as denoisers. Recent work has reported …