Information Theoretical

Information theory is increasingly used to analyze and improve various machine learning tasks. Current research focuses on applying information-theoretic principles to optimize data representation (e.g., developing efficient sentence embeddings), enhance privacy and fairness in data processing, and improve the efficiency of deep learning models through active learning and data subset selection. This approach offers a principled framework for understanding and addressing challenges related to data compression, model robustness, and resource constraints, leading to more efficient and reliable machine learning systems.