Optimal Compression

Optimal compression research aims to minimize data size while preserving information fidelity, crucial for efficient data storage, transmission, and processing. Current efforts focus on adapting compression techniques to diverse data types (images, graphs, neural network models), employing methods like neural networks, bits-back coding, and adaptive quantization/pruning strategies to achieve optimal rate-distortion trade-offs. These advancements are significantly impacting fields such as distributed learning, robotics, and multimedia communication by reducing computational and communication costs, enabling faster and more efficient applications.