Large Number

Research on "large numbers" spans diverse fields, focusing on efficiently handling and interpreting datasets or systems with a vast number of elements, whether clusters, classes, agents, or data points. Current efforts concentrate on developing scalable algorithms and models, such as those based on stable matching, mirror descent, and determinantal point processes, to address computational challenges arising from high dimensionality and complexity. These advancements are crucial for improving the performance and applicability of machine learning techniques in various domains, including image processing, natural language processing, and multi-agent systems, while also providing insights into the underlying statistical properties of large-scale data.