Constructive Reduction

Constructive reduction focuses on efficiently decreasing the size or complexity of data or models while preserving essential information. Current research explores this across diverse fields, employing techniques like deep learning frameworks for dimensionality reduction and continuous representation (e.g., in climate data analysis), information-theoretic approaches for pruning neural networks and causal model simplification, and optimal transport methods for data augmentation and dimensionality reduction. These advancements improve data storage, accelerate model inference, enhance explainability, and enable the application of complex models to resource-constrained environments, impacting fields ranging from climate science and anomaly detection to reinforcement learning and medical imaging.