Minimum Description Length

Minimum Description Length (MDL) is a principle for model selection that favors models which concisely represent data, thereby mitigating overfitting. Current research applies MDL to diverse problems, including network reconstruction, representation learning, clustering, and symbolic regression, often employing Bayesian inference, hierarchical models, or greedy search algorithms to optimize model complexity. This principle offers a powerful, theoretically grounded approach to automating parameter selection and improving generalization performance across various machine learning tasks, leading to more accurate and interpretable models in fields ranging from biology to cosmology.