arXiv:2511.18945v4 Announce Type: replace Abstract: We propose a fully data-driven approach to designing mutual information (MI) estimators. Since any MI estimator is a function of the observed sample from two random variables, we parameterize this function with a neural network (MIST) and train it end-to-end to predict MI values. Training is performed on a large meta-dataset of 625,000 synthetic joint distributions with known ground-truth MI. To handle variable sample sizes and dimensions, we employ a two-dimensional attention scheme ensuring permutation invariance across input samples. To qu
The proliferation of advanced AI models demands more robust methods for understanding complex data relationships, which MIST addresses by leveraging neural networks for mutual information estimation.
Accurate mutual information estimation is crucial for tasks like model interpretability, feature selection, and causality discovery, directly impacting the development and reliability of advanced AI systems.
The ability to estimate mutual information robustly and data-drivenly across variable sample sizes and dimensions could improve the foundations of machine learning research and application.
- · AI researchers
- · Machine learning engineers
- · Data scientists
- · AI model developers
- · Traditional MI estimation methods
- · Data-intensive tasks lacking robust interpretability
MIST provides a more accurate and scalable method for understanding statistical dependencies in high-dimensional data.
Improved mutual information estimation could lead to more efficient and interpretable AI models, accelerating research in areas like causality and reinforcement learning.
This could enable breakthroughs in scientific discovery and complex system control where understanding information flow is paramount, potentially influencing sectors like synthetic biology and advanced materials.
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