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
Evaluating Large Language Models for Generalization and Robustness via Data Compression
Yucheng Li, Yunhao Guo, Frank Guerin, Chenghua Lin
Tropical Decision Boundaries for Neural Networks Are Robust Against Adversarial Attacks
Kurt Pasque, Christopher Teska, Ruriko Yoshida, Keiji Miura, Jefferson Huang
Develop End-to-End Anomaly Detection System
Emanuele Mengoli, Zhiyuan Yao, Wutao Wei
Benchmarking Transferable Adversarial Attacks
Zhibo Jin, Jiayu Zhang, Zhiyu Zhu, Huaming Chen
Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features
Aku Kammonen, Lisi Liang, Anamika Pandey, Raúl Tempone
The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness
Mengyao Du, Miao Zhang, Yuwen Pu, Kai Xu, Shouling Ji, Quanjun Yin
Improving Pseudo-labelling and Enhancing Robustness for Semi-Supervised Domain Generalization
Adnan Khan, Mai A. Shaaban, Muhammad Haris Khan
On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge
Chin-Hung Chen, Boris Karanov, Wim van Houtum, Wu Yan, Alex Young, Alex Alvarado
Enhancing Reliability of Neural Networks at the Edge: Inverted Normalization with Stochastic Affine Transformations
Soyed Tuhin Ahmed, Kamal Danouchi, Guillaume Prenat, Lorena Anghel, Mehdi B. Tahoori