Unknown Distribution

Research on unknown distributions focuses on developing methods to effectively model and predict outcomes when the underlying data generating process is not fully understood. Current efforts concentrate on online learning algorithms that adapt to incoming data without strong distributional assumptions, employing techniques like optimal transport and Riemannian geometry to analyze and manipulate probability distributions. These advancements are crucial for improving the robustness and reliability of machine learning models in real-world applications where data often deviates from idealized assumptions, impacting fields like portfolio optimization and image processing.