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A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications
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 …
signal processing and machine learning (ML) applications. It is used for solving optimization …
SF-FWA: A self-adaptive fast fireworks algorithm for effective large-scale optimization
Computationally efficient algorithms for large-scale black-box optimization have become
increasingly important in recent years due to the growing complexity of engineering and …
increasingly important in recent years due to the growing complexity of engineering and …
Simultaneously optimizing perturbations and positions for black-box adversarial patch attacks
Adversarial patch is an important form of real-world adversarial attack that brings serious
risks to the robustness of deep neural networks. Previous methods generate adversarial …
risks to the robustness of deep neural networks. Previous methods generate adversarial …
Revisiting zeroth-order optimization for memory-efficient llm fine-tuning: A benchmark
In the evolving landscape of natural language processing (NLP), fine-tuning pre-trained
Large Language Models (LLMs) with first-order (FO) optimizers like SGD and Adam has …
Large Language Models (LLMs) with first-order (FO) optimizers like SGD and Adam has …
Derivative-free reinforcement learning: A review
Reinforcement learning is about learning agent models that make the best sequential
decisions in unknown environments. In an unknown environment, the agent needs to …
decisions in unknown environments. In an unknown environment, the agent needs to …
Gradient-free methods for deterministic and stochastic nonsmooth nonconvex optimization
Nonsmooth nonconvex optimization problems broadly emerge in machine learning and
business decision making, whereas two core challenges impede the development of …
business decision making, whereas two core challenges impede the development of …
Sparse-rs: a versatile framework for query-efficient sparse black-box adversarial attacks
We propose a versatile framework based on random search, Sparse-RS, for score-based
sparse targeted and untargeted attacks in the black-box setting. Sparse-RS does not rely on …
sparse targeted and untargeted attacks in the black-box setting. Sparse-RS does not rely on …
Adaptive and communication-efficient zeroth-order optimization for distributed internet of things
This article addresses the optimization problem of zeroth-order in a distributed setting,
where the gradient information is not available in the edge Internet of Things (IoT) clients …
where the gradient information is not available in the edge Internet of Things (IoT) clients …
Adaptive momentum variance for attention-guided sparse adversarial attacks
The phenomenon that deep neural networks are vulnerable to adversarial examples has
been found for several years. Under the black-box setting, transfer-based methods usually …
been found for several years. Under the black-box setting, transfer-based methods usually …
Optimizing molecules using efficient queries from property evaluations
Abstract Machine learning-based methods have shown potential for optimizing existing
molecules with more desirable properties, a critical step towards accelerating new chemical …
molecules with more desirable properties, a critical step towards accelerating new chemical …