Data Transformation

Data transformation involves modifying datasets to improve machine learning model performance, fairness, or efficiency. Current research focuses on developing transformations that enhance model robustness (e.g., against adversarial attacks or data corruption), mitigate biases, and optimize model training and inference (e.g., through dimensionality reduction or efficient encoding). These techniques are crucial for addressing challenges in various applications, including improving the accuracy and explainability of models in sensitive domains like healthcare and finance, and enabling efficient deployment of large models on resource-constrained devices. The impact of data transformations on model generalization and the development of theoretically grounded methods for selecting optimal transformations are also active areas of investigation.