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 …

Recent studies have shown that adversarial examples in state-of-the-art image classifiers
trained by deep neural networks (DNN) can be easily generated when the target model is …

With the emergence of a spectrum of high-end mobile devices, many applications that
formerly required desktop-level computation capability are being transferred to these …

Abstract High quality Machine Learning (ML) models are often considered valuable
intellectual property by companies. Model Stealing (MS) attacks allow an adversary with …

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 …

As application demands for zeroth-order (gradient-free) optimization accelerate, the need for
variance reduced and faster converging approaches is also intensifying. This paper …

Recent work demonstrated the promise of using resistive random access memory (ReRAM)
as an emerging technology to perform inherently parallel analog domain in-situ matrix …

Distributed multiagent optimization finds many applications in distributed learning, control,
estimation, etc. Most existing algorithms assume knowledge of first-order information of the …

The adaptive momentum method (AdaMM), which uses past gradients to update descent
directions and learning rates simultaneously, has become one of the most popular first-order …

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 …