Near Optimal Degree Testing

Near-optimal degree testing focuses on efficiently determining the complexity of underlying structures, such as the maximum in-degree of a Bayesian network or the sparsity of a polynomial, given limited data. Current research emphasizes developing algorithms with near-optimal sample complexity, often leveraging techniques like testing-by-learning and employing estimators based on spectral properties or χ² divergence. These advancements improve the accuracy and efficiency of hypothesis testing in various domains, including database dependency analysis, model calibration assessment, and machine learning model evaluation, leading to more reliable and resource-efficient analyses.