Representation Complexity

Representation complexity investigates the difficulty of representing functions or data within different computational models, focusing on the trade-off between accuracy and model simplicity. Current research explores this in various contexts, including symbolic regression (using neural networks and evolutionary algorithms to discover concise mathematical expressions) and reinforcement learning (analyzing the complexity of representing models, policies, and value functions). Understanding representation complexity is crucial for improving the efficiency, interpretability, and generalizability of machine learning models, impacting fields ranging from scientific discovery to engineering applications. This involves developing new metrics for quantifying complexity and designing algorithms that explicitly manage this trade-off during learning.