Native Robustness
Native robustness in machine learning focuses on developing models inherently resistant to various forms of input perturbations, including adversarial attacks and noisy data, without relying solely on post-hoc defenses. Current research emphasizes techniques like ensemble methods, reprogramming existing models, and modifying training procedures (e.g., using different learning rates for specific model layers or incorporating regularization methods) to improve robustness across diverse model architectures, including convolutional neural networks, vision transformers, and large language models. This field is crucial for deploying reliable AI systems in safety-critical applications, such as healthcare and autonomous driving, where model resilience to unexpected inputs is paramount.
Papers - Page 8
Bridging Interpretability and Robustness Using LIME-Guided Model Refinement
Navid Nayyem, Abdullah Rakin, Longwei WangOptimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Tasks
Jiacheng Hu, Xiaoxuan Liao, Jia Gao, Zhen Qi, Hongye Zheng, Chihang Wang
Robustness of Large Language Models Against Adversarial Attacks
Yiyi Tao, Yixian Shen, Hang Zhang, Yanxin Shen, Lun Wang, Chuanqi Shi, Shaoshuai DuBreaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature
Yichen Wang, Yuxuan Chou, Ziqi Zhou, Hangtao Zhang, Wei Wan, Shengshan Hu, Minghui Li
On the Robustness of Spectral Algorithms for Semirandom Stochastic Block Models
Aditya Bhaskara, Agastya Vibhuti Jha, Michael Kapralov, Naren Sarayu Manoj, Davide Mazzali, Weronika Wrzos-KaminskaOn the Robustness of Distributed Machine Learning against Transfer Attacks
Sébastien Andreina, Pascal Zimmer, Ghassan KarameA Black-Box Evaluation Framework for Semantic Robustness in Bird's Eye View Detection
Fu Wang, Yanghao Zhang, Xiangyu Yin, Guangliang Cheng, Zeyu Fu, Xiaowei Huang, Wenjie Ruan