Conditional Entropy

Conditional entropy, a measure of uncertainty in a variable given knowledge of another, is increasingly used in machine learning to assess model predictability, quantify uncertainty types (aleatoric and epistemic), and improve model robustness. Current research focuses on developing accurate conditional entropy estimators for regression and classification tasks, applying it to enhance various machine learning techniques like conformal prediction, out-of-distribution detection, and causal discovery, often within frameworks involving optimal transport or deep generative models. These advancements offer improved model interpretability, more reliable uncertainty quantification, and more effective handling of real-world data complexities, impacting fields ranging from anomaly detection to reinforcement learning.