Differentiable Prompt Learning for Vision Language Models
Zhenhan Huang, Tejaswini Pedapati, et al.
IJCAI 2025
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.
Zhenhan Huang, Tejaswini Pedapati, et al.
IJCAI 2025
Ching-yun Ko, Pin-Yu Chen, et al.
ICML 2023
Karan Bhanot, Dennis Wei, et al.
ESANN 2023
Alex Gu, Tsui-Wei Weng, et al.
NeurIPS 2020