Robust Classifier

Robust classifiers aim to build machine learning models that are resilient to various forms of noise, uncertainty, and adversarial attacks, maintaining high accuracy even under challenging conditions. Current research focuses on improving robustness through techniques like adversarial training, meta-learning to mitigate spurious correlations, and employing architectures such as diffusion models and randomized smoothing. These advancements are crucial for deploying reliable machine learning systems in real-world applications where data quality and distribution shifts are common, impacting fields ranging from image recognition to safety-critical systems.