Robust Method

Robust methods in machine learning aim to develop algorithms and models that maintain high performance despite noisy data, adversarial attacks, or other uncertainties. Current research focuses on improving robustness across diverse applications, including image analysis (using techniques like atlas registration and diffusion models), regression (addressing the bias-variance trade-off), and deep learning (e.g., through multi-module architectures and loss function modifications). These advancements are crucial for enhancing the reliability and trustworthiness of AI systems in various fields, from medical image analysis and remote sensing to cybersecurity and reliable engineering design.