Data Perturbation

Data perturbation involves intentionally modifying datasets to improve model robustness, enhance privacy, or address data scarcity. Current research focuses on developing methods to mitigate the negative impacts of perturbations, including adversarial training, data augmentation techniques (like noise addition or synthetic data generation), and novel algorithms like Benders decomposition for efficient privacy-preserving data release. These advancements are crucial for improving the reliability and trustworthiness of machine learning models across diverse applications, from medical image analysis and natural language processing to time-series forecasting and recommendation systems.