2025/10/23 by Yanzhi Chen, Zijing Ou, Chen, Yanzhi +5 · 2 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural Networks and Applications #Time Series Analysis and Forecasting #Wireless Signal Modulation Classification
paper · pdf · doi:10.48550/arxiv.2510.20968
openalex publication_date 2025/10/23 · openalex created_date 2025/10/28 · openalex updated_date 2026/07/28
Estimating mutual information (MI) is a fundamental task in data science and machine learning. Existing estimators mainly rely on either highly flexible models (e.g., neural networks), which require large amounts of data, or overly simplified models (e.g., Gaussian copula), which fail to capture complex distributions. Drawing upon recent vector copula theory, we propose a principled interpolation between these two extremes to achieve a better trade-off between complexity and capacity. Experiments on state-of-the-art synthetic benchmarks and real-world data with diverse modalities demonstrate the advantages of the proposed estimator.