Visual Prompting for Adversarial Robustness
Aochuan Chen, Peter Lorenz, et al.
ICASSP 2023
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 problems similarly to gradient-based methods. However, it does not require the gradient, using only function evaluations. Specifically, ZO optimization iteratively performs three major steps: gradient estimation, descent direction computation, and the solution update. In this article, we provide a comprehensive review of ZO optimization, with an emphasis on showing the underlying intuition, optimization principles, and recent advances in convergence analysis. Moreover, we demonstrate promising applications of ZO optimization, such as evaluating robustness and generating explanations from black-box deep learning (DL) models and efficient online sensor management.
Aochuan Chen, Peter Lorenz, et al.
ICASSP 2023
Kristjan Greenewald, Yuancheng Yu, et al.
NeurIPS 2024
Ioana Baldini Soares, Chhavi Yadav, et al.
ACL 2023
Pratik Vaishnavi, Kevin Eykholt, et al.
USENIX Security 2022